Defensive deception is a promising approach for cyberdefense. Although
defensive deception is increasingly popular in the research community, there
has not been a systematic investigation of its key components, the underlying
principles, and its tradeoffs in various problem settings. This survey paper
focuses on defensive deception research centered on game theory and machine
learning, since these are prominent families of artificial intelligence
approaches that are widely employed in defensive deception. This paper brings
forth insights, lessons, and limitations from prior work. It closes with an
outline of some research directions to tackle major gaps in current defensive
deception research.
them to inducing making, act leveraging defensive core the of of ideas research cybersecurity realized been in the non-trivial have been efforts develop deception techniques.
promising controls access as inside and outside or controls subverting of line distinct defense deception idea of key decision their to mislead Since the benefits have deception there community, defensive
the with misleading them Second, machine been that mimic
2番目に誤解を招く機械は
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to objects techniques have information or
物体へ 技術には情報がある
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lure paper was motivated on
ルアー 紙は動機づけられた
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focusing attackers. to obtain more how game-theoretic
焦点 攻撃者だ ゲーム理論を)もっと理解する
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observed defender
オブザーバーディフェンダー
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game-theoretic strategies deception attackers or strategies.
ゲーム理論戦略 攻撃者や戦略を欺く。
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are and Two main techniques tion attacker of an based on defensive for fusion mal or poor defensive decoy objects information Therefore, understanding defensive ML-based existing stood the in contributions of our we papers, discuss deception techniques the them in and paper
order compared paper to survey the existing and the clarify section.
紙の順序を比較して 既存の部分と明確な部分を調べる。
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following have been distinguish existing the on papers differences between
以下 既存の論文の相違点を区別し
0.68
in-depth or underthe key survey defensive our
深く、または重要な調査の下、我々を防御する
0.49
B. Comparison with Existing Surveys
B。 比較 現存 調査
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et at as as
など に として として
0.59
the the de-
はあ? はあ? de-
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well goal, layer, Several
まあ ゴール 層 数
0.52
including defensive techniques monitoring
含む 防御 技法 監視
0.73
survey 107, on 118].
調査107 第118話。
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deception unit, and surveyed theoretical models
偽装ユニット 理論モデルを調べ
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a conducted have [9, as 69, follows defensive surveyed
実施されたhad[9, as 69]は、防御調査に続く
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application, or data deception, however, Spafford [9] in considered
しかし、考慮されたスパフォード[9]は、適用、またはデータ偽造
0.67
paper discussed of affecting for a defender this survey integrating In addition,
調査統合におけるディフェンダーへの影響に関する論文
0.47
studies techniques ception techniques al.
インセプション テクニックを勉強するアル。
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et [69] Han decriteria, based four on used deception.
et [69]han decriteria, based on used deception.
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They ployment of for generation, defensive deception placement, deployment, and monitoring of deception elements.
彼らは偽装要素の生成、防御的な偽装の配置、展開、監視を企図した。
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Han deceptive al.
Han deceptive al
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various techniques the deployed Their network, discussion comprehensive.
様々な技術 ネットワークを展開 議論を包括的に
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of how defensive a defense security following the technique: deception and and the models attacker’s an achieve to a paper is limited decepdefensive didn’t this work should be techniques.
survey on defensive cybersecurity different types defense, target engagement.
防衛サイバーセキュリティに関する調査 様々な種類の防衛 ターゲット・エンゲージメント
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Their attacker and 2008–2018, over classification their own techniques.
彼らの攻撃と2008-2018は、彼ら自身のテクニックを分類した。
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work obfuscation as discussed paper developing for Nash, analysis conducted analysis without where ML-based these two insights
nashのための論文で論じられた作業の難読化,mlベースで解析を行なわずに分析した2つの考察
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discussed the tradeoffs between in whether to relation they are system, layers.
システムとレイヤの関係性に関するトレードオフについて議論した。
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game-theoretic not is Almeshekah and presented been has deception cyber the specific, be To domain.
ゲーム理論上の not は almeshekah であり、提示されたものは、deception cyber the specific、be to domain である。
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the authors discussed considering three phrases in a defensive planning, and integrating, implementing this evaluating.
著者らは、防衛計画において3つのフレーズを検討し、これを統合し、実装した。
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particular, In in deceptions of planning terms can perception misled which be security system’s goals.
特に、計画用語の誤解において、セキュリティシステムの目標と誤解されることがある。
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However, and to modeling contribution its of set limited tion a to attackers.
しかし、コントリビューションをモデル化するためには、攻撃者に限定的なtion aをセットする。
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variety consider of a network implementing considered in et Pawlick al.
variety consider of a network implementing in et Pawlick al.
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deception defensive deception techniques and of and honey-X, obfuscation, mixing, published 24 paper papers to taxonomies defined develop game-theoretic defensive of deception is interesting subcategories under the deception defensive game signaling and existing the of game-theoretic are in this paper to considering realistic network deception defensive techniques game (i.e., theory and ML) may promising research and directions.
偽装防御技術とハニーxの難読化、混合、24の論文 to taxonomies to develop game-theoretic defense of deceptionという論文は、偽装防御ゲームシグナリングと既存のゲーム理論の下、興味深いサブカテゴリであり、現実的なネットワーク・デセプション防御技術ゲーム(すなわち理論とml)を考えるための論文である。
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Lu Recently, et al. [90] defensive of processes the
lu、最近、等々。 プロセスの[90]防御.
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and deception. This used approaches techniques, such as Stackelberg, the theories.
偽りも これは、理論であるstackelbergのようなアプローチ技術を用いた。
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However, survey and defensive techniques game-theoretical environments or combination provide more
しかし,ゲーム理論的な環境や組み合わせはより多くを提供する。
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environments which defensive deception an conducted extensive game-theoretic and been used the main
防御的デセプションが実行された広範なゲーム理論とメインとして使用される環境
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[107] taxonomies that have authors discussed
著者が議論した[107]分類
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for six perturbation, moving defensive game-theoretic
6つの摂動 動き 防御ゲーム理論
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privacy. The deception
プライバシー。 偽り
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brief survey of consisting a deception
構成する簡単な調査 deception (複数形 deceptions)
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surveyed related treat moving target
関連調査 移動目標を扱い
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of useful based three categories:
役に立つ ベース3 カテゴリー:
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conducted common defense limited
実施 共通 防衛 limited～
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This on to
これ オン へ
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英語（論文から抽出）
日本語訳
スコア
TABLE I COMPARISON OF Key
テーブル 私 比較 鍵
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Criteria OUR SURVEY PAPER WITH THE EXISTING SURVEYS OF DEFENSIVE DECEPTION Our
基準 我々の ディフェンス・デセプションの現況調査報告
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Han [90] [69]
藩 [90] [69]
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Lu Survey (2020)
ル 調査(2020年)
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et al. (2020)
など。 (2020)
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Pawlick [107]
Pawlick [107]
0.85
al. et (2019)
アル et (2019)
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et al. (2018)
など。 (2018)
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Rowe Rrushi
Rowe (複数形 Rowes)
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and [118] concepts Provides Provides key taxonomies Includes ML approaches game-theoretic Includes Describes attack types Describes metrics Describes Is Discusses Discusses Discusses
included. information deception, and discussed dissimulation to
含まれてる 情報 欺き 悲観を議論し
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planning of deception, authors The
偽りの計画、著者たち
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phases: ment of ception.
フェーズ: 受容の段階です
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on information simulation to discussed game-theoretic discussing cused on research.
研究に焦点をあてたゲーム理論的議論を議論するための情報シミュレーションについて
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However, was defensive deception Rowe and Rrushi in niques of terms excuses, and social false on background the deception detectability of the calculation deception.
virtual), or goal ultimate activeness and in-depth an provides and insights on goal.
仮想) 目標 究極のアクティブネス 目標に関する提供と洞察を深く掘り下げること。
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security physical the following classification
警備 物理的 以下の分類
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key contributions: scheme characthat technique in of its terms deception an presence of object categories, effects applying after expected attack or protection for (i.e., asset or passive (i.e., or both).
deception defensive ML-based types of metrics We what game-theoretic used testbeds in less or based deception techniques their tiveness We limitations niques limitations tions research.
deception defense ml-based types of metrics we what game-theoretic used testbeds in less or based deception techniques their tiveness we limited niques limit tions research (英語)
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the efficiency. discussed from this work.
はあ? 効率性。 この作品から議論された。
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Based we and techand direcfor game-theoretic and ML-based defensive deception
ベースは私たちです そして、ゲーム理論とMLベースの防御的騙しのテクノロジーと希薄化
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learned and deception defensive insights on these promising future
これらの有望な未来に対する学習と欺きの防御的洞察
0.59
are different challenges of disdomains and game-theoretic or
ドメイン分割とゲーム理論の異なる課題です
0.53
existing techare more or deception
既存の技術は多かれ少なかれ
0.61
more defensive and experiment or MLeffec-
より防御的で 実験かMLeffec
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observed in learned, by deception
学習で観察され 騙して
0.52
deception the attacks defensive
だまされた 防御攻撃
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lessons the extensively techniques
レッスン? 広範囲 技法
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suggested examined literature surveyed
示唆 精査 文学 surveyed～
0.67
surveyed insights
surveyed～ 洞察
0.72
prove and the
証明しろ そして はあ?
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the are by
はあ? は ところで
0.53
in to Note veying techniques learned deception approaches
イン へ 偽装アプローチを学習した注意ベイイング技術
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of that this scope (GT) game-theoretic discussing extensive are
この範囲(GT)のゲーム理論に関する議論は
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and from techniques or ML are paper or ML-based insights, survey.
技術やMLは 紙またはMLベースの洞察、調査。
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using not that in excluded this
除外して使うのではなく
0.61
on surfocused deception defensive or limitations, lessons some Hence, defensive game-theoretic either survey paper.
deception pros and key the develop defensive extensively section
deception pros and key the development defensive extensive section
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leveraging ML in techniques.
MLをテクニックで活用する。
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In deception existing MLthe surveys addresses and their
既存のMLを欺いて調査する
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Paper is paper structured the concept of deception and taxonomies deception.
紙は、偽りと分類の偽りという概念を構成した紙である。
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the key technique.
鍵となるテクニックだ
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Structure of the of The this rest provides Section II related to defensive Section III discusses deception defensive the clarifies key compared to techniques, deception goal.
defense a that achieve same in components Section gameusing key the explains IV existing gamethe theoretic defensive deception and surveys techniques theoretic along the discussions cons.
コンポーネントセクションでこれを達成する防御 a gameusing key the explains iv existing gamethethethethetheo retic defense deception and surveys technique theoretic along the discussion cons. (英語)
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Section V components techniques addition, defensive based cons.
セクション5 コンポーネントテクニックの追加、防御ベースのcons。
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pros and VI Section describes isting game-theoretic techniques.
pros と VI セクションでは、ゲーム理論の技法を記述している。
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presents metrics VIII Section of efficiency the game using theory veys evaluation defensive Section defensive different cyberphysical Internet of Things and wireless Section the answering addition, In this defensive the and suggests
deception addition, In this validating for surveyed in how theoretic game techniques been have domains, as such enterprise (CPS), web-based cloud software-defined networks
deception VII discusses deception application systems (IoT), networks.
deception vii では deception application systems (iot) ネットワークについて論じている。
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IX summarizes learned by and lessons Section I-D. raised in found from limitations in surveyed this work directions.
IX の要約 learn by and lessons セクション i-d. では、この作業の方向性を調査した。
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and techniques sursection those existing this work.
既存の作品の技法を 分析するのです
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or developed insights questions discusses the techniques future
発展させたり 洞察の質問は 未来の技術について
0.51
ML-based for networks, networks, (SDNs),
MLベースのネットワーク、ネットワーク、(SDN)
0.67
existing and ML. used techniques
ML と ML。 使用技術
0.66
key section deception promising
有望なキーセクションのデセプション
0.60
by countered exthe deception defensive
偽りの防御に逆らうことで
0.62
types ML-based
type ML ベース
0.76
the research attack and
この研究は 攻撃して
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effectiveness to measure
有効性 to measure
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techniques defensive research testbeds
技法 防御 研究 試験ベッド
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II. TAXONOMIES OF DEFENSIVE DECEPTION
II。 タキソノミ 防御的デセプションの
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Deception has been heavily used by attackers which perform of applications both a variety section, we computer our perspective.
認識は、様々な部分の両方でアプリケーションを実行する攻撃者によって多用されている。
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defender’s discussions
defender (複数形 defenders)
0.51
attacks of at social and deception of
社会や欺くことへの攻撃
0.66
levels In this a of terms different systems.
このa項のレベルは 異なるシステムです
0.71
in limit in
in limit イン
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3 explain the addition, we In of process the in developing their and clarify distinctive
3 追加を説明します、我々は、その発展と明確化の過程において、
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related defensive characteristics. common
関連する防御特性 共通
0.69
taxonomies
taxonōmāte
0.61
used techniques deception
使用技術 deception
0.79
to A. with refers of conflict
へ A。 と 参照 紛争の
0.65
attacker concept of Defensive Deception
攻撃的概念 防御的デセプションの
0.75
of mislead intents, [123].
誤解を招く意図の [123].
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interactions situations one’s mission spectrum under
相互作用の状況 mission spectrum (複数形 mission spectrums)
0.66
concept to intentionally weaknesses,
意図的な弱さの概念です
0.63
deception military conventional about enemy an taken [123].
敵が奪った[123]という 通常の偽装軍事
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tactics and and the employs deception to manipulate enemy’s deception Defensive between an The [123].
戦術及びその使用は、敵の[123]の間の偽証を操るために偽証を使用する。
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Concepts to The one’s actions The strengths actions defender applies advance to and to a wide a of defender defensive deception initiated in military environments has been the name domains cyber cyberdecepintroduced Spafford [10] refined and Almeshekah tion.
その人の行動に対する概念 強固な行動の防御は、軍事環境から始まった防御的デセプションの広い範囲に前もって適用され、サイバーサイバーデプテント・スパフォード(cyber cyberdecepintroduced spafford [10] refined and almeshekah tion という名称で呼ばれている。
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concept of “planned as [147] by Yuill taken cyberdeception to and attackers to confuse and/or mislead them to that specific actions computer-security take take) (or not the concept of deception defenses.” Although is highly multiagreed behind idea common the disciplinary deception is a way and belief form a to entity an to mislead as false control behavior deception [118].
its based on it Therefore, when successfully executed, is a deceivee suboptimally, provides achieving deceiver benefits which in for a deceiver’s ‘maRrushi goal.
Rowe the and deception of concept as the nipulation’ core a deceivee encourages something wants or to do discourages deceiver doing from deceivee the not want.
deception or of Defensive Deception Taxonomies cerwith First all, of even can be we intent), non-malicious (either intent tain defensive of the in deception concept consider the context intent’ ‘with deception of discussions limit deception, we atagainst although the intent to defend a given system the of terms taxonomies discuss section, we tackers.
欺くか、または 防御的デセプション(defenderal deception taxonomies, cer with first all, or even can be we intent, or even can be we intent)において、非軍事的(in deception concept(in deception concept, in deception, in deception)の意図は文脈的意図(context intent)を考える。
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in this In are (ii) used; techniques following aspects: (i) what conceptual (i.e., A true or not whether created is object fake object successfully to deceive); (iii) what of an deceiving the goal opponent; system; such as detecting an attacker or protecting a deception, for (mainly and attack detection) protection) to deceive an
i) 概念的(i) 創造物が偽物であるかどうかの判定に成功するかどうか、(iii) 目標を欺くかどうか、(iii) 攻撃者を検出したり、(主に、攻撃検出) 保護のために、偽物を保護するなどのシステム 訳抜け防止モード: in this In is (ii ) used ; techniques following aspects : (i ) what concept (i.e.) 生成したオブジェクトが偽物であるかどうかが偽物であるかどうか ) (iii) 相手を欺くもの,システム 攻撃者を検出したり、詐欺を防いだりすること for(主に攻撃検出)保護 deceive (複数形 deceives)
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hidden to effects (iv)
効果に隠された(iv)
0.83
deceive or are what expected when ultimate is
だます、または何か ultimate (複数形 ultimates)
0.45
deception for
deception for...
0.37
a exists but given deception uses
存在するが 詐欺の使用は
0.61
is attack (v) whether true object
攻撃だ (v) true object
0.70
active given
アクティブ ですから
0.61
an is a a a
アン は あ あ あ
0.55
we passive (mainly
私たち 受動的 (主に)
0.58
or opponent. Conceptual Deception Categories: 1) 26, 57], 28, following techniques: [10, Masking • or in data the Repackaging something else vulnerability a
4 Activeness Passive Passive Passive Passive Active Active Active Passive Active or
4 活動性 受動受動受動能動能動能動能動・受動能動能動能動能動
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Active Passive Passive Passive
活動 受動的 パッシブ・パッシブ
0.49
• • • • • in
• • • • • イン
0.80
to as as can
へ として として できる
0.60
trap such [148],
トラップ そんな [148],
0.63
creating lure a an attacker’s
創造 ルアー attack (複数形 attacks)
0.51
real a nodes
real a ノード
0.78
be which the real be
というと the real be―the real be
0.56
of aspects real imitating honeypots
現実的な側面を ハニーポットを模倣する
0.45
object presented allows
object presented allow
0.85
overshadowing object.
オーバーシャドーイングオブジェクト。
0.62
a new fake can it,
新しい偽物が できるのか?
0.64
hide something background or be to object can be hidden real, undetected the as confused be by
背景を隠したり、物に近づいたりすることは、現実に隠され、混乱しているように見つからない
0.61
files honey create can defender files.
ファイルhoney create can defender ファイル。
0.79
regular like look files that different way is a 26]: This [10, Dazzling with to hard that in blend is something as repackaged else.
通常のルックファイルのように、違うやり方で26個ある:これは[10]、ブレンドが再パッケージ化されているように、ハードにぶらぶらしている。 訳抜け防止モード: regular like look file that different way is a 26 ] : This [ 10] ブレンドに固執するのは、他のパッケージと同じようなものです。
0.81
A aiming it, to by example, an true For intruder error messages.
例えば、真のFor侵入者エラーメッセージを目指しています。
0.61
receiving many 26]: This refers to [10, Mimicking often look to used is This object.
多数の26]を受け取る:これは[10]を意味し、よく使われる模倣は、このオブジェクトである。
0.71
to interfaces or attackers.
インターフェースや攻撃者に対してです
0.59
can create 26]: A defender [10, Inventing a software instance, For attackers.
26]: 攻撃者のためのディフェンダー[10, ソフトウェアインスタンスの発明]。
0.57
to download in attackers for honeypot to data.
データにハニーポットの 攻撃者をダウンロードする
0.65
collecting personal attackers’ the attracts Decoying [10, 26]: A deceiver attention be should that away protected a critical assets from provide can system.
the For classical defensive conceal to honeypots user-monitoring their normal machines.
The For classical defense hidden to honeypots user- monitoring their normal machines.
0.93
like look more an This deceives [57]: by Camouflage background environment.
これは [57]: カモフラージュの背景環境によるものです。
0.50
For into a or real object can programs files be and valuable attacker it difficult for an to make information (or disinformation) information [57]: Fake False be can planted to mislead an attacker cyberspace.
Deception Information: or Objects Actual Presence 2) actual no or exists object true an applied when can be actual, use defender specific, more be object exists.
偽装情報: or objects actual presence 2) real no or exists object true a applied when be actual, use defender specific, more be object exists
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a can true objects or information but may want to hide it by lying or a even example, For towards information providing if it.
a は真のオブジェクトや情報を提供することができるが、嘘や偶例によってそれを隠したいかもしれない。
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false system as an operating system but may lie it the certain OS in deed.
オペレーティングシステムとしての偽のシステムは、あるosの行為を偽っている可能性がある。
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On the uses Unix. But fake other a not can hand, confusion, creating uncertainty or the purpose of created for be suboptimal reach to opponent an lead which a can poor files.
honey or tokens as honey decision, such Deception: Expected Effects (or Intents) of Defensive 3) expected effects more or deception, defensive are one Via making deception, of goal the achieving of the process as strategies.
We its in choice suboptimal choose an attacker a categorize these effects in terms of the following five aspects: Hiding: to change Some to slow complexity [10, Luring demonstrated object real an honeypots are to information [52]: Most Misleading as last step misleading an Blending: This into blended relies on an to things of
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0.64
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0.72
impractical. be to considered set knowing the attack of due realistic not to is hand, the context.
現実的じゃない 現実的でない攻撃を知るための集合は、手、文脈である。
0.43
On other practical represents a class players may or may not know instance, [77].
他の実践では、クラスプレーヤーは、例えば[77]を知らないかもしれないし、知らないかもしれない。
0.56
players other For other players’ types, strategies,
他のプレイヤーは、他のプレイヤーのタイプ、戦略、
0.76
more the know incomplete games where information not
もっと知りたいのは 情報がない不完全なゲーム
0.66
dynamic of is information assumption of to taken be uncertainty
不確実性であるとする情報仮定の動的
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or multistage in case sometimes plete the defender The its by adversary actions given in a inherent information game an one or of about some fully player may a functions.
a game information what know not given in players other a track prohibitive the to However, game.
あ ゲーム情報 プレイヤーに与えられていないもの 他のトラックでは、toゲームは禁止される。
0.59
and strategies, imperfect inoften game is attacker and a
不完全な不正ゲームは攻撃者であり
0.52
multistage types, a complete, information between an
多段階型、完全で、ある間の情報
0.59
as sysdynamic Players acting upon Partial Observability: 4) this In games.
として sysdynamic players acting on partial observability: 4) ゲームにおけるこれ。
0.70
and modeled dynamic are tems stochastic and time over system/environment the case, state changes in actions However, by on depends the taken all the players.
observe will players more one or scenarios, some fully not game.
プレイヤーはより多くまたはシナリオを観察し、一部は完全にゲームではない。
0.61
If observable partially a in This state.
この状態で部分的に観測可能ならば。
0.64
the results factor, an environment the system is affected by exogenous partially which the state transition results is in a special the observable case In [70].
stochastic to a partially of POSG with only a the game turns deals observable Markov POSG model with different capture as game as well information Therefore, imperfect monitoring).
stochastic (POSG) game single player, process dynamic general form of as such incomplete (i.e., information such game efficiently
確率的(ポグ)ゲーム単一プレイヤー、そのような不完全(すなわち、効率的なゲーム情報)のダイナミックな汎用形式 訳抜け防止モード: stochastic (POSG ) game single player, process dynamic general form of such incomplete (i.e.) ゲームに関する情報を効率よく
0.94
the most settings, [39].
設定が一番です [39].
0.64
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あれ ゲーム それでも
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アン は あ あ 5)
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両方の適用例 境界選手
0.58
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合理性: 期待は2つのメインに制限される
0.53
its to maximize only has player search to find Strategies:
プレイヤー検索のみを最大限に活用し 戦略を探せます
0.80
bounded and between rational an game attacker and a defender.
ゲームアタッカーとディフェンダーの間に 縛られている。
0.53
A rational player can always choose an strategy utility.
合理的なプレイヤーは常に戦略の効用を選べる。
0.72
However, optimal a rational and resources bounded cannot unlimited [131].
しかし、最適有理と資源は[131]を無制限にできない。
0.73
an afford action game in strategies or Mixed Pure 6) pure pure theory are [131].
戦略における手頃なアクションゲームまたは混合純粋6)純粋純粋理論は[131]である。
0.81
A mixed or strategy with determined means strategy the strategy player’s a probability or probability 0 a a mixed strategy contrast, 1.
Zhu theory. game Stackelberg using used et identified attacks jamming paths routing deceptive against but an optimal routing considering resource allocation based on strategies.
朱説。 game stackelberg using used et identified attack jaming paths routingceptive against but a optimal routing considering resource allocation based based based based strategy (情報通信)
0.77
mixed Uncertain Future Reward 7) by multiple utility
混合不確定な将来の報酬(7) 複数ユーティリティによる
0.64
strategy is the control
戦略は コントロールは
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guaranteed
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player’s
player (複数形 players)
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caused あ 実用性-研究や環境を欺くことができない変数-
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inherent the [131].
131]に固有のものです
0.57
uncertainty conditions game-theoretic defensive non-stationary by caused mainly
不確実性条件 主に引き起こされるゲーム理論の防御的非定常
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不確実な将来の状態は
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0.57
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間 防御 だまされた
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動く ターゲット 防御!
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by MTD techniques. used concept our on understanding the functionalities and defensive deception, MTD, and hold view based on Fig related to
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as extensively to model framework cybersecurity problems: a defense strategies,
として サイバーセキュリティ問題: 防衛戦略の枠組みを 広くモデル化することです
0.61
in modeling the defender strategies,
防衛戦略をモデル化することです
0.70
attack and opponent’s key as actions system and an these discuss
相手の鍵を攻撃して 行動システムとこれらの議論は
0.76
cybersecurity
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elements games used of
要素 ゲーム 使用 ですから
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or A. Key Game security elements of players, of a We follows.
あるいは A。 プレイヤーの鍵となるゲームセキュリティ要素は次のとおりである。
0.75
1) player. Players:
1) プレイヤー。 プレイヤー:
0.78
Pawlick where or a
Pawlick (複数形 Pawlicks)
0.50
game-theoretic taking research Most defensive game two-player a model approaches deception where the using a defender an players two and attacker are defensive two-player even a in 104].
However, 93, [81, deception game, types of different modeled deception some games players modeled [104] Zhu and each under twoa as a signaling player a sender defender game can be while honeypot either a normal an system attacker as the on type.
しかし、93,[81,deception game, different modeled deception 一部のプレイヤーは[104]Zhuをモデル化し、それぞれ2a未満のシグナリングプレーヤとして送り手ディフェンダーゲームは、通常またはシステムアタッカーをオンタイプとしてハニーポットしながら行うことができる。
0.77
Depending has receiver a defender type, the one takes its strategy.
レシーバーにはディフェンダータイプがあり、その戦略を踏襲する。
0.57
Pawlick et [105] introduced a three-player cloud defender, an attacker and a device which game between a is connected with the false to deceive signals the Complete (In) 2) players game allows parameters, such as function, reward a players,
A complete information game the knowledge of to be taken by other game the of state current
完全情報ゲームは、他のゲームによって取られるべき知識を状態電流とする
0.87
cloud where attacker. Information: full to have possible actions the and
雲だ 攻撃者だ 情報: 可能なアクションを行なえるよう全力を尽くす。
0.67
defender send can
守備 送って できる
0.60
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それぞれに モーションゲーム
0.56
opponent by actions) (or and non-deterministic by caused is both the uncertainty players.
行動による敵) (または、原因不明の非決定論は、不確実なプレイヤーの両方である。
0.55
In a signaling type [97, 104] where and player its opponent movement of an type.
シグナリングタイプ[97,104]において、相手のタイプの動きをプレーする。
0.74
opponent’s its knows it after action take can player an a is action, optimal an take player For it to a Utilities: 8) reward) or own both know to critical (or its payoff utility function and its opponent’s payoff function.
against’s knows it after action take can player a is action, optimal a take player for it to a utilities: 8) reward) あるいは own both know to critical (or its payoff utility function and its opponent’s payoff function)。
0.79
In a static, oneprofiles action on based strategy a game, time is reward [131].
ゲームベースの戦略に基づいた静的なoneprofilesアクションでは、time is reward [131]。
0.72
take) can actions of (i.e., a set player of all players reward repeated game), a a However, in a sequential game (i.e., action can history of profiles from the on based estimated be such in game.
テイク)シーケンシャルゲームにおけるaのアクション(つまり、すべてのプレイヤーのセットプレイヤーが繰り返しゲームに報酬を与える)、または、ゲームにおけるオンベースで推定されるプロファイルの履歴(すなわち、アクション)。 訳抜け防止モード: take ) can action of (つまり、すべてのプレーヤーのセットプレーヤーが繰り返しゲームに報酬を与える) a) しかし、シーケンシャルなゲーム(つまり、アクションはゲームにおいて、ベースから推定されるプロファイルの履歴をそのようにすることができる。
0.79
Hence, the of the a games, end the to beginning or average expected the to maximize player accumulated aims [25].
sequential over reward of the period the game game A full represents game Subgame: 9) Full Game or actions to set a considers each where possible all of player indicates utility.
A subgame its to maximize action one select of subset considers player a the subset a of full game where a to maximize its all possible actions when selecting one action includes Particularly, a sequential utility.
サブゲーム its アクションを最大化する サブセットの1つの選択は、プレイヤー a をフルゲームの部分集合 a とみなし、a は1つのアクションを特にシーケンシャルユーティリティを含む場合に可能なすべてのアクションを最大化する。
0.67
games, subgame to similar and node single a its [131].
ゲーム、サブゲーム、類似ゲーム、ノードシングルが[131]です。
0.74
is It refers tree-structure in a subtree a which game, sequential used [74] and game.
これは、ゲームが[74]とゲームで連続的に使用されるサブツリーのツリー構造を指す。 訳抜け防止モード: It is referred tree - structure in a subtree a which game 74 ] と ゲーム を逐次使用します。
0.86
form an to extensive House Cybenko an hypergame the theory to interactions the between to model uses defender.
Hypergame and attacker a a corresponding the different player’s each game strategies [153] under the addition, and Xu and Zhuang [142] used Subgame Perfect Nash Equilibsubgame) rium (SPNE) deal with to and attacker in
Game-Theoretic Defensive Deception this In game-theoretic discuss section, we the tion techniques used literature.
game-theoretic defense deception game-theoretic discussion section, we the tion techniques used literature(英語)
0.75
We in classification to game-theoretic discuss protection: techniques asset for deception obfuscation, eynets, objects, honey technologies, fake used empirical studies flow.
我々は,ゲーム理論的な議論の保護を分類する。 詐欺の難読化のためのテクニック アセット, eynets, objects, honey technologies, fake used experience studies flow。 訳抜け防止モード: we in classification to game - theoretic discuss protection 騙しの難読化, エイネット, オブジェクト, ハニー技術のための技術資産 偽の使用経験的研究フロー。
0.73
Some human are players settings where game show them how theory to defensive empirical deception in human-in-the-loop.
一部の人間はプレイヤーの設定であり、ゲームは人間のループにおける実証的詐欺の理論を示す。
0.62
A Honeypots: defensive is mainly and high between detectability by
ハニーポット: 防御性は、主に検出性によって高い
0.66
honeypot deception used two (HHs) interaction based on LHs and are HHs cost.
ハニーポットの騙しはLHsに基づく2つの(HHs)相互作用を使用し、HHsコストである。
0.61
deployment and LHs; However, than attackers detectability [69].
デプロイメントとLH; しかし、攻撃者検出性[69]よりも高い。
0.61
LHs than cost higher deployment game to signaling employed a and [104] Pawlick an where attacker system defense honeypot-based a develop investigated authors The honeypots.
lhs than cost higher deployment game to signaling used a and [104] pawlick a where attack system defense honeypot-based a developed investigation authors the honeypots. (英語)
0.85
detect to has ability the or without games with multiple models evidence of signaling or is when complete information available not.
信号の複数のモデルを持つゲームの有無、または、完全な情報が得られない場合に検出する。
0.74
In the cheapsignaling game with deception a talk games with evidence (i.e.,
偽りの安いシグナリングゲームでは、証拠(つまり、証拠)を伴うトークゲームを行う。
0.56
most studied A in the honeylow interaction [69].
多くはハネロー相互作用[69]で研究した。
0.61
The different provide HHs
異なるHHを提供する
0.90
common honeypot (LHs) pots differences deception lower incurs
common honeypot (lhs) pots difference deception lower incurs (複数形 common honeypots)
0.66
has been strategy forms: honeypots HHs
ハニーポットhhsという戦略形態です
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文学として ずっと 朱
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(すなわち、送信者)
0.66
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確率のモデル化です 無費用で検出できる
0.60
signaling (i.e., an honeypot) with
ミツバチとのシグナル(すなわちハニーポット)
0.47
comreceiver), deception the that ability to detect deception does not necessarily lower defender’s
虚偽を検知する能力が必ずしも被告の能力を低下させるとは限らない。
0.54
detection), extended from cheap-talk games munication the receiver (i.e., a attacker’s utility.
the down to evidence game with been has The signaling [106, or selection in creation other works model the evidence used [106] a game with signaling 111].
beenのダウン・トゥ・エビデンスゲームは[106, or selection in creation other works model the evidence using [106] a game with signaling 1111]である。
0.73
Pawlick type and sender on estimated is based a where signaling message.
Pawlick型と推定値の送信者は、 where シグナルメッセージに基づいています。
0.65
The authors extended the a transmitted deception.
著者は送信された偽装を拡張した。
0.54
evidence probabilistic detector with a game with of can which attackers this However, signal is vulnerable to deception and the to leading signal, the analyze detecting introduced a leakage.
deception-based defense mechproposed The de(DoS) Denial-of-Service retrieve the attract attacker sigintent.
deception-based Defense mechproposed The de(DoS) Denial-of-Service retrieve the attract attacker sigintent。
0.78
the used real equilibrium to model perfect Bayesian the Basak attacker.
本物の均衡を使って 完全なベイズ人バサック攻撃をモデル化した
0.63
defender and et al.
ディフェンダーとアル。
0.34
type an identify to attacker tools The strategies.
攻撃ツールにidをタイプして戦略を実行します。
0.57
defensive better when in and his actions goals game-theoretic Through ap(e g , honeypots) attackers early
inとそのアクションがap(honeypots)攻撃によるゲーム理論を早期に達成した場合の防御性向上 訳抜け防止モード: in, and his action goal game -theoretic through ap(e, ハニーポット) 攻撃者は早く
0.70
C¸ eker anism to fender deploys about information games with naling between the interactions cyberdeception used [24] take possible as as early encapsulated type attacker is campaign.
[93] used a non-cooperative Bayesian information is follower.
[93]非協力ベイズ情報の利用は従者である。
0.67
towards game. that honeypot honeypot the attacker attack graphs performance model and considered research.
ゲームへ。 ハニーポット ハニーポット 攻撃者はグラフを攻撃し 研究を検討
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an authors motivation, et [81] al.
著者のモチベーション, et [81] al.
0.70
honeypots, deploy strategies to extended The game.
ハニーポット、ゲームを拡張する戦略を展開。
0.71
selection additional allows selection game are strategies in which attack conducted authors The [86, performance the compare analysis of disadvantages and advantages discussed game-theoretic models in the defensive
Aggarwal et rational incomplete information game making in the presence of with two timing-based deception by applying early or in the rounds of games and of extent and deception.
aggarwal et rational incomplete information game making in presence with two timing-based deception by applied early or in the rounds of games and of extent and deception. 英語)
0.82
the deception using amount of attack decrease effectively using a different timing any introduce in difference allocation honeypot Various have been considers and
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et Nan [99, should that defender a for tions deceiving the performing a for be used fake nodes.
et nan [99, that defender a for tions deceive the performing a be used fake node.”【イディオム・格言的】
0.73
attacker into expending Cons: Pros and defensive popular honeypot decades.
乱暴なCons: プロと防御的な人気ハニーポットへの攻撃。
0.56
over Since technology which deception vulnerabilities, additional introduce is aim its not by the honeypots, the if an attacker successfully (i.e., components system can honeypots intelligence attack additional assets) system with detection can which intrusion new lead signatures.
However, maintaining attack honeypots incurs experformance potential a tra
しかし アタックハニーポットの維持は 性能を低下させる
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the nodes computation task while resources attacking is most has matured any lured existing
資源が攻撃されている間、ノードの計算タスクは、最も成熟したもので、
0.58
heuristic 100] to intelligently select
ヒューリスティック100] インテリジェントに選択する
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保護する; 保護する; 保護する
0.53
the collecting improving approach. provided Nash
収集の改善は 接近 提供 Nash
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その騙しは ゲーム、アンウォー
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ゲームモデル その為
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addition, authors there cost.
加えて 作者 そこ コスト。
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A is 9 existence to due the paths routing or honeypots of intelligent, as more becomes it recently, for honeypots effectively complexity and cost.
A は 9 ルーティングの経路や 知性のあるハニーポットの存在 最近になって より複雑でコストのかかるハニーポットが 訳抜け防止モード: A は 9 インテリジェントな経路またはハニーポットの存在 最近では どんどん増えていきますが ハニーポットの複雑さとコストは効果的です。
0.75
El-Kosairy Honeywebs: framework that provides detect malicious
El-Kosairy Honeywebs: 悪意を検知するフレームワーク
0.81
of resource have been sophisticated the honeypots, consumption.
資源は洗練され ハニーポット 消費
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such as However, In investigated.
しかし、調査ではそうである。
0.57
little have attackers been develop to challenging attackers in terms
攻撃者が攻撃者に挑戦するために 開発されているものはほとんどありません
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more deceiving
more deceiving
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drawbacks degradation additional the addition, emerged realistic of both 2) server a honeyweb to traffics to deception decoy defender the and Pros based on honeypots fake assistant be
and web a web application firewall with and forward suspicious traffics Typically, this combines and token, honey honeypot, action the main definition, of a (VM) consume to
web web アプリケーションファイアウォール 疑わしいトラフィックを転送する 一般的に、これは、(vm) が消費する、主要な定義である、 and token、honey honeypot、action the main definitionを組み合わせたものです。
0.65
including game a resources.
ゲーム・ア・リソースを含む。
0.58
are approaches. tokens or while cost
アプローチです トークン、またはコスト
0.56
technologies. [65] an attacker defender) players in as a The is based framework on information.
技術。 [65] 攻撃者ディフェンダー) 情報に基づいたフレームワークとしてプレイするプレーヤー。
0.72
In their game placement about the of probe the target host to attacker’s probability.
彼らのゲームでは、攻撃者の確率にターゲットホストを配置する。
0.66
The compromising and a equilibrium strategy how they are to system.
システムに対する妥協戦略と均衡戦略です
0.52
architecture particularly rarely They used assist honey files.
建築 特にハチミツのファイルを 使うことは滅多にない
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Developing honey less its an honeypots.
ハチミツの栽培は ハチミツよりも少ない
0.44
Hence, not defensive and Grosu (i.e., a game.
したがって、防御とグロス(つまりゲーム)ではない。
0.80
imperfect
imperfect～
0.73
honeypot technologies, the implementing effort and Cons: Honeywebs game-theoretic along with incurs the used without other Garg Honeynets: 3) and a honeynet system non-cooperative strategic games extensive deception defender makes model, the can the a honeypot while some roles with true their identify probing of considers utility both the cost the mixed studied honeypot.
honeypot technologies, the implementing effort and cons: honeywebs game-theoretic with inusing the without other garg honeynets: 3) and a honeynet system non-cooperative strategic games extensive deception defender makes model, the can the a honeypot. (英語)
0.57
They host or showed games these solutions of and honeynet the strategies of the determine a proposed Dimitriadis honeynet [55] on architecture This an attacker-defender game.
emulation, their gateway each are considered as and individual players.
エミュレーション、それぞれのゲートウェイは個々のプレイヤーと見なされる。
0.75
The security each of identify is player payoff to used equilibrium.
それぞれが識別するセキュリティは、使用済み均衡に対するプレイヤーの報酬である。
0.65
a Nash and Cons: Pros is honeynet The a system and architecture inside with to mainly deal designed attackers allow a system manager to monitor threats and learn from them.
A Nash and Cons: Pros is honeynet システムとアーキテクチャは、主に設計された攻撃者を扱い、システムマネージャが脅威を監視し、そこから学ぶことができる。
0.81
The Project Honeynet example typical a is [129] honeynet of consisting of multiple the honeynet works with multiple honeypots, how to optimally deploy them needs more investigation based additional and deception of deployment
The [125] of an designed inference sensitive entities.
125] 推論に敏感なエンティティを 設計しました
0.53
This attacks. They information design
これ 攻撃だ 情報デザインや
0.72
modeled a obfuscation adaptive
難読化適応のモデル化
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attacker together and work both the on costs.
攻撃者 共に オンコストで 働きます。
0.61
Obfuscation: between a
難解:aの間
0.55
英語（論文から抽出）
日本語訳
スコア
linear provided
linear～ provided
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caused by to search a minimum
最小の探索によって引き起こされる
0.74
can observe valuable information of users and noises obfuscation.
ユーザの貴重な情報と ノイズの難読化を観測できます
0.64
They program solutions an optimization problem that provably achieves for utility
彼らはユーティリティのために確実に達成できる最適化問題をプログラムで解決する
0.60
bounds. a defensive zero-sum, analyzed
境界だ 分析された防御ゼロサム
0.59
privacy those under loss al.
プライバシーは損失アルだ
0.46
et proposed [71] Hespanha non-cooperative, on work based information.
etは[71] hespanha non-cooperative, on work based informationを提案した。
0.72
They with partial manipulate game can competitive a the maximize to its opponents found They that there deception.
部分的な操作ゲームを持つ彼らは、敵に最大限の競争力を与えることができる。
0.71
be to presented to information Cons: Pros and Compared as such honey-X techniques, obfuscation is easy data into normal noise adding defender legitimate a or research obfuscation how to hide real attacker.
be to presented to information Cons: Pros and Compared as such honey-X techniques, obfuscation is easy data into normal noise add defender legitimate a or research obfuscation how hide real attacker。
0.85
framedeception games stochastic in defender how a available information to defensive of the effectiveness an exists optimal amount of deceive effectively an attacker.
deception defensive to other the techniques, key benefit of low cost.
偽造は他の技術に 防御する 低コストの鍵となる利益だ
0.71
However, deployability with also information can a data hand, other the On of develop aims a to how rather than an
しかし、情報も含むデプロイ性はデータハンドになり得るし、他のOn of Developmentは、どのようにしてデータハンドではなく、どのようにデプロイするかを目標にしている。
0.37
confuse most technique to detect
ほとんどのテクニックを混同して検出し
0.52
user. mainly information
ユーザー。 主に情報
0.74
a the an to device
あ はあ? と 装置
0.51
Deception consider interactions games and
だまを考える インタラクションゲームと
0.56
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The a defender and attacker between to model the interactions are model used signaling where compromised be cloud which may the the between designed and probability.
[142] a Xu Zhuang with certain a defender where an attacker and a the attacker between game and target of vulnerability investigate to a the the has ability the to technologies defensive apply can change the defender studied mainly work This device.
certain of a vulnerability attackers’ preparation for launching an attack affects how the of effectiveness rethe strategies.
攻撃を開始するための攻撃者の準備の一部が、その効果が戦略にどのように影響するかを判断する。
0.59
As deception defensive the subgame perfect Nash Equilibrium authors sult, the applied the the analyze to (SPNE) terrorist’s the and costly learning leveraged a [145] al.
偽装の防御として、サブゲーム『パーフェクトナッシュ・ナッシュ』の著者たちはsultを、分析をテロリストの『the and costly learning』に応用し、[145] alを活用した。 訳抜け防止モード: 欺きの防御として the sub game perfect nash equilibrium authors sult, the applied the analysis to (spne ) terrorist ’s. (英語) 145 ] al を利用した。
0.68
et Yin secrete recourse resource to game Stackelberg a and defender.
et yin secrete recourse resource to game stackelberg a and defender(英語)
0.79
The a attacker of analysis a using ical defender.
icalディフェンダーを使用した分析のアタッカー。
0.60
They the played calculated defender and the when attacker the can attacking.
プレイヤーが計算したディフェンダーと攻撃者が攻撃する時。
0.67
strategic defender’s fake to mislead attackers.
戦略的なディフェンダーは、攻撃者を誤解させる偽物だ。
0.54
They the interactions authors conducted both pure and mixed optimal provided an rate accuracy the of deploy unlimited surveillance
defensive and hypergame, where game an ulated by asymmetric an the attacker deception strategies while
防御とハイパーゲーム:ゲームaが攻撃者が欺く戦略を非対称に表現する
0.76
does 10 a
そうでしょう 10 あ
0.61
by a a defender.
ところで あ あ ディフェンダー
0.45
information. attacker an set strategies
情報だ 攻撃者an 戦略設定
0.70
the introduces opponent. knowledge
紹介は 相手。 知識
0.63
other players have defender
他のプレーヤーはディフェンダーを持っています
0.39
game This theory and Hor´ak for network deception one-sided
ゲーム この理論と Hor ́ak for network deception one-sided
0.75
deception Technologies: cyberdeception a
デセプション技術:サイバーデセプションa
0.56
structure. an abstract deception signal is deception model concept a to to defender.
構造。 抽象デセプション信号はディフェンダーに対するデセプションモデル概念aである。
0.71
Due the nature a flexibility high
柔軟性が高い性質のため
0.81
and payoff true Cons: and Pros way game game to a apply of the an between attacker can that it formulation, has game abstracted other hand, the techniques.
and payoff true cons: and pros way game game to a applied of the an between attack can that it formula, have game abstracted other hand, the technique.”【イディオム・格言的】
0.79
On deception of use various types problem in cybersecurity to model challenging it a a quite is the as particular context proposed deception game framework specific not does use defensive 6) General Cyberdeception of by
techniques. existing Some to technologies [73] et al.
テクニック。 existing Some to Technology[73] et al.
0.71
and security on strategy an POSG (partially perfect has defender imperfect In sequences take and actions of to obtain or attempt opponent actions true that assumed work This rational a and opponent its towards its actions of knowledge has no a network attacker hurdle to identify a target and a network.
POSG (Partially perfect has defender imperfect In sequences take and action to obtain or attempt opponent action thatsum work This rational a and opponent its towards its action of knowledge have no network attacker hurdle to identify a target and a network。 訳抜け防止モード: 戦略上のセキュリティ posg (部分的には完全で、シーケンスが不完全である) 相手の行動を真に獲得または試みる行為 この合理的なaを仮定し、その知識の行動に対して反対する ターゲットとネットワークを識別するためのネットワーク攻撃のハードルはありません。
0.73
the of in presence its defender aware is improve deception defensive to [45] al.
被告が認識している存在は 詐欺防御を[45]alに改善させる
0.65
discussed SDN environbased an on dependability and system security technologies.
sdn 環境ベースの on dependability とシステムセキュリティ技術について論じた。
0.63
The authors ment utilizing a set honey of to requirements critical the are discussed what effective realize evaluation methidentified and defensive promising deception and Zhu and including metrics ods evaluation testbeds.
a where is attacker a the is leader and the defender a follower.
a where is attack a is leader and the defender a follower.”【イディオム・格言的】
0.69
they (MDP) Process Through an Markov Decision investigated of an attacker noncritical points.
They (MDP) Process through an Markov Decision investigated to a attacker non critical points。
0.78
Pros and combined defender on based of attacks.
攻撃に基づくプロとコンバインドディフェンダー。
0.55
perform multi-staged Cons: defensive multiple Since defender’s a set of used and a as against to choices defend can make more that different merits to different can introduce deal with APT attackers which to In particular,
multiple profiles, or capture to game Bayesian and an attacker advance information one-shot proposed their Eastman (TE)
複数のプロファイル、またはゲームベイズに捕獲し、攻撃者がイーストマン(TE)を提案した一発の情報
0.66
are the attackers types can defensive
攻撃者は防御できるのか?
0.68
performance on
performance (複数形 performances)
0.31
in this device deceptions strategies,
この装置では 偽装戦略
0.62
of Tennessee a The the
テネシー州の ザ・ザ・ザ・
0.42
investigated simulation,
調査 シミュレーション
0.56
deception various
deception 様々な
0.79
types attacks, the
種類 攻撃! はあ?
0.55
potential behavior
potential 行動
0.79
using of static
利用 ですから static
0.70
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はあ? 作者 に
0.58
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あ は など ですから
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optimal Mohammadi provide more combing more
最適 モハンマディ もっと火を付けて
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deal with techniques it not defensive
防御的でない技術を扱う
0.73
relevant than deployment games with strategy the in external
配備よりも 対外戦略を持つゲーム
0.55
additional identify a techniques.
追加で テクニックを特定できる
0.64
Objects: resources to deception one costs.
対象: 1つのコストを欺くためのリソースです
0.56
In addition, of multiple combination
加えて、複数の組み合わせ
0.77
deception can them. However, introduces trivial to deception Fake 7) signaling player optimal an ing avatar use can an an identify to users.
欺くことはできる。 しかし trivial to deception fake 7) signaling player は ing アバターの使用を最適なものにすることでユーザを識別することができる。
0.70
This game optimal an derived expected the iment, the the avatar or 105], games [104, (i.e., a second mover where (i.e., sender) The key information.
本ゲームは、誘導された期待されたiment、アバターまたは105]、ゲーム104(すなわち、送信者が鍵情報となる第2の移動機)を最適とする。 訳抜け防止モード: このゲームは、導出予想のイメント、アバターを最適にする または 105 ], game [ 104, ( i.e., a second mover where ( i.e., sender ) キー情報。
0.79
uncertainty for creating which identity a fake the not or whether [38] et al.
偽のidを作成するための不確実性は、[38]などである。
0.68
Casey and a defender attacker agent that may a hostile order In system.
Caseyとディフェンダー攻撃エージェントは、敵対的なオーダーInシステムである可能性がある。
0.65
the whole proposed authors a honey a basic designing by agent interaction the model between [37] Casey et al.
various A under user internal game as fake signaling external with interacting attacker by considered the payoff two players and expertheir In alarms.
various A under user internal game as fake signaling external with interacting attack by consider the payoff two player and expertheir In alarms。
0.80
raising of threshold actions guide a metric payoff is used to of other works defender.
閾値アクションの引き上げは、他のワークディフェンダーのメトリックペイオフが使用される。
0.62
Unlike using signaling their signaling games put the defender as a a first mover is an attacker receiver) while incomplete with played are the games is of idea using the signaling games the attacker because the defender uses the make can about doubtful attacker a receiver user.
シグナリングゲームを使用する場合とは異なり、ディフェンダーを第1のムーブラーとして配置することは攻撃者受信機である)が、プレイが不完全な場合は、ディフェンダーが攻撃者に対して疑わしい攻撃を行うため、攻撃者のシグナリングゲームを使用する。 訳抜け防止モード: シグナリングゲームを使うのと異なり、ディフェンダーは最初のムーバーとなった 攻撃者レシーバー ) プレイが不完全なのはゲームです 攻撃者は make を受信者の疑わしい攻撃者に使用するため、攻撃者はシグナリングゲームを使うのが理想的です。
0.73
is real between interactions discussed They of an organization.
議論された相互作用の間は現実的です。
0.58
surface organization obtain of this kind to mitigate the confuse to surface signaling compliance and agents the insider the signaling game to with
an insider considered harm and threats, the malicious game to organization.
組織への悪質なゲームとして 危害と脅威を考慮に入れたインサイダー。
0.63
an threat in the model intelligence and a
モデルインテリジェンスとAの脅威は
0.61
discussed further applied a agents
エージェントのさらなる適用について
0.66
learning al. a machine operating
学習 アル あ マシン操作
0.58
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多項式 一般サムゲーム, モデル
0.64
a et [132]
あ など [132]
0.65
introduced a the (CCGs), Camouflage Games Cyber performing attacker and an defender between the defender can mask The sance as information, an such fake network attacks.
To reconnaissance of effect to mitigate introduced optimal they the Fully Approximation Scheme (FPTAS) given constraints Mixed Integer Linear Program (MILP) to search for strategy.
HD is number utility elberg strategy The results significantly in a Pros a as
hdはナンバーユーティリティのelberg戦略で、pros aでかなりの結果が得られた
0.66
The HE honeybots. of honeybots The the that the network.
ハニーボット。 ハニーボットはネットワークです。
0.33
Although deception of optimization model.
だまされてはいるが 最適化モデルです
0.48
game based on showed decrease
表示された減少に基づくゲーム
0.69
large and defensive social Cons:
大きくて防御的な 社会的な意見:
0.62
using technique considered and chose PAS information a small infected
技術の利用 小さい感染したPAS情報の検討と選択
0.72
infiltration of social SODEXO so-called
社会的SODEXOの浸透
0.63
social The (SOcial of Honeybot consisting and Protec(HE), a moderate composed of and dynamics the by botmaster Stackdeployment optimal an gathered by honeybots.
social the (social of honeybot composed and protec(he)は、by botmaster stackdeployment(英語版)の中間的構成とダイナミクスである。
0.57
can honeybots number of population (i.e., botnet)
ミツバチの個体数(すなわちボットネット)を
0.46
identities costly less
アイデンティティーのコストは
0.55
or and avatars rela-
または アバター・レラ
0.54
fake is a a 11
偽物は あ あ 11
0.62
a and patches confusion
あ そして パッチ 混乱
0.63
additional alarms. of
さらに 警報だ ですから
0.57
boundary common is patches be users and work has such as extra attackers
境界共通 パッチは ユーザーや仕事には 余分な攻撃者や
0.66
In deception Patches: traditional (2)
偽りのパッチ:伝統的(2)
0.76
administrator Enterprise to the program misbehavior vulnerabilities security to
administrator enterprise to the program misbehavior vulnerabilities security to
0.83
patched, system vulnerabilities of As the past.
As the pastのパッチ、システム脆弱性。
0.78
from patch fake a provided the analyze try that to technique patch fake is input parts.
patch fake aから、patch fakeのテクニックを試す分析は、入力部品である。
0.75
One to normal it can introduce However, little increase false fake identities, the adverse effect using or users producing legitimate for confusion intelligent of effect addition, the been detectability studied.
not has compohas patch mainly A honey two vulnerasoftware known to patch fix fake to an to mislead code attacker patches are fake [19].
compohasパッチは、主にhoney two vulneraソフトウェアで、不正なコードのアタッカーパッチに偽の修正をパッチすることが知られている [19]。
0.80
In the literature, [19]. [16] or ghost patches that can the term patches honey ability the has it but patch; also allow and a attacker decoy, to the success.
文学では[19]. [16]またはゴーストパッチ(ghost patch)は、ハニー能力にパッチを当てることができる。 訳抜け防止モード: 文学では[19]. [16 ]またはゴーストパッチ という用語は、ハニー能力を持っている人にパッチを当てることができます しかし、patch ; also allow and a attack decoy, to the success。
0.65
In addition, the authors server a to web scale honey patches.
さらに、サーバaからwebスケールのhoneyパッチも作成する。
0.63
lack [19]. releasing contain an by solution, technique historical designed vulnerabilities.
欠落[19]. リリースには解決方法と 歴史的に設計された脆弱性が含まれます
0.57
a creating decoy attacker’s the file decoy is part a is misleads the evaluated analysis
regular same as function as a redirect efficiently to an attacker to achieve a fake allowing provided strategy a regular transfer into systems are often exposed by the vulnerabilities due checking subtle of way the eliminate to A patches.
as However, the security of and types vulnerability location the obtain a blueprint could attacker patches the security analyzing Spafford [19] Avery and attackers the for misleading this patches.
for bogus control flow the change not engineering, reverse the output but does this experiment, their program.
ボグ制御フローでは エンジニアリングではなく 出力を逆転させる この実験は 彼らのプログラムだ
0.77
was technique In program where in [33] the program runtime and this the measure of technique.
33] プログラムのランタイムと,それがテクニックの尺度であるプログラムのテクニックです。
0.71
[20] and Wallrabenstein Avery evaluated (i.e., deceptive of three patches faux, response) by into the them the which module security techniques that they found while security proposed tance of assessing patches security meaningful Cho based game deception and attacker defender a work game.
This decision its affect can affect can which the used work Stochastic of the hypergame where for misleading strategy deception a choose fake the patches and can compromise, which to lead Pros and Cons: Honey patches techniques tion
effectiveness the and active obfuscated, game-based proposed addition, In guidelines.
有効性とアクティブな難読化ゲームベースの追加提案。
0.55
to are meet the unable the emphasizing imporbased on a clear and a modeled [48] al.
クリア及びモデル化された[48]alに基づいて強調することができない。
0.55
et which theory hypergame in an different given perceptions in a (mis)perception how a player’s take, strategies choose to making the in utility player’s given This probabilistic model a Perti Nets to build patches defender uses fake as a believing in to attacker the node non-vulnerable target as failure.
real packets developing defensive network flow are: applying lenges of of and risk increase may packets congestion deceptive the (2) packets; real for delay the delivery the extra incur of and transfer to capacity limited a node source generate has and real requiring packets, a balance between and flows.
a Their altruistic choosing when the improves overall sources.
ソース全体を改善するとき、利他的な選択をする。
0.55
node behavior considered a et al.
ノードの挙動を考慮に入れる。
0.62
[155] Similarly, Zhu selecting sending deceptive real for paths routing a and the source the flow, deceptive node is rate deceptive flow as well real of and as flow.
[155] 同様に、zhuは経路をルーティングする経路とフローの源に対して偽りの現実を送ることを選択し、偽りのノードは偽りのフローである。 訳抜け防止モード: [155 ] 同様に Zhu selecting も a をルーティングする経路と、流れの源である deceptive node に対して deceptive real を送る 浮動小数点数の流れと 流れの速さです
0.84
work introduced the such the Stackelberg of equilibrium equilibria.
研究はそのような平衡平衡のスタックルベルクを導入した。
0.63
the Miah system, performing reconnaissance called attacker between attacks.
攻撃間攻撃と呼ばれる偵察を行うmiahシステム。
0.71
and the Stackelberg the non-zero-sum deceptive signaling game.
そして、stackelberg the non-zero-sum deceptive signaling game。
0.75
in cyber-physical framework to deal with APT attacks systems.
APT攻撃システムを扱うサイバー物理フレームワーク。
0.59
system via obtain attacks the Under to aiming bait this attacks, reconnaissance scanning or the attackers.
system via get the under to aim bait this attack, reconnaissance scanning or the attacks.”【イディオム・格言的】
0.70
information This the to game-theoretic hierarchical concept of solved defender does the semi-definite programming problem where a perfect not considering partial noisy or observations Cons: Pros and to approach deception be actions which cannot such ception technologies, However, fingerprint.
情報 この解決されたディフェンダーのゲーム理論的階層的概念は、パーフェクトが部分的なノイズや観察を考慮しないという半確定的なプログラミング問題を行う。 訳抜け防止モード: 情報 this the to game -theoretic hierarchy concept of solve defender does the semi-defined programming problem, a perfect not consider partial noise or observations cons: pros and approach deception be action しかし、このような認識技術は、指紋ではできない。
0.81
and real fake network and performance network we III, techniques, and
本物の偽のネットワークや パフォーマンス・ネットワーク 第三部、技術、そして
0.77
congestion of other that results proved of utility the source single Before flows.
ソースのシングル before flow が有効であることを証明した他のコングリゲーション。
0.69
choose allowed to the as the path of concepts, solution equilibrium (PSE), the rate counterparts strategy and mixed their game.
選択は、概念の経路、解均衡(pse)、利率対応戦略、ゲームの混合として許される。
0.65
Their such exist that results there proved et al.
それらは、その結果が証明されている。
0.64
[95] designed deceptive network flow a Snaz, to mislead the attacker interaction the They model two a with players and Bas¸ar [121]
or by goal. the towards defensive flow is network malicious mitigate effectively defended deby traditional as malicious network flow scanning highly to it balance is flows additional that degradation.
However, scientists decision making cognitive some also subjects human using experiments empirical assigned which consider to defenders or attackers as game.
et Cranford [50, experiment empirical between an game realize signaling to a and attacker defensive defender where the uses defender signals, deception limited with where players humans are reflecting cognition, theory.
et cranford [50, experiment experimental between a game realize signaling to a and attack defense defender, using defender signal, deception limited with players humans are reflecting cognition, theory.] (英語)
0.77
bounded To in rationality game measure the effectiveness techniques, of deception defensive Ferguson-Walter [62] [64] also al.
bounded To in rationality game measure the effect technique, of deception Defense Ferguson-Walter [62] [64] also al.
0.89
and Ferguson-Walter conducted team memexperiment with empirical red bers different at study network a participants in types instance, For deception scenarios.
of decoy devices are described explicitly for of deceptive showed that to the participant.
デコイの装置は 参加者に見せた偽装で 明確に説明されています
0.66
The defensive techniques of aware took they participants the when are any taking shows time much before more that move slower to deception made them Aggarwal tool, simulation study developed [4] et to a al.
reconnaissance participants network in attacks a timing introducing of the studied effect showed intervals.
偵察参加者ネットワークの攻撃 研究効果の導入タイミングは 間隔を示しました
0.77
Their results performed honeypots more on attacks the may theorists game Pure a is game not experiments empirical deception game studies However, we discuss these chance to give be aware of a can that experiments game at traditional of a game theory, their bounded rationality, players, corresponding can
5月理論家ゲームpure a is game not experiment experimental deception game studies(英語版) しかし、これらの機会は、伝統的なゲーム理論におけるゲーム実験、彼らの有界合理性、プレイヤー、対応するcanのcanに注意を向けるものである。
0.77
existence results the deception, action.
存在は偽り、行動をもたらす。
0.50
This and attack. called HackIt, stage deception that the than often argue that this game-theoretic
これと攻撃。 HackItという,このゲーム理論をよく主張するステージ詐欺
0.65
empirical game. to empirical subjects-based structure follow the partly more two consisting or of their opponent strategies and decision making.
various domains, extensively been behaviors system section, we first and approaches defensive deception
様々なドメイン 広範囲にわたる行動システム 第一に 防衛上の詐欺にアプローチし
0.71
become more in applications cybersecurity.
アプリケーションのサイバーセキュリティが向上します
0.62
ML techniques have including learning for adopted automating attacks domain in the cyberdeception In this discuss the key to implement ML-based steps survey extensive ML-based on an conduct literature.
A. of Implementing ML-Based Defensive Deception Key Steps have been Generation: Dataset 1) malicious detecting used mainly in based activities on or honeypots [128].
attacks which make datasets; it annotated datasets.
攻撃は データセット; 注釈付きデータセット。
0.58
have been more using ML-based should step to includes
MLベースの should の使用がより多くなっています
0.69
based complete datasets are
ベース 完全なデータセットは
0.60
obtaining However, trivial on the often confidential due organization owning unknown and related applications Pre-Processing: Datasets learning ahead of from the This reduction Feature data goal the accuracy.
Training: Training dataset process ing the of [68, 117].
トレーニング: [68, 117]のトレーニングデータセットプロセス。
0.53
the testing Testing: dataset performance evaluate trained [117].
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彼らはtwitterで、ハニーポットを使ったmlアルゴリズムのアカウントとキーを調査した。 訳抜け防止モード: 彼らはTwitterのアカウントを調べ、MLアルゴリズムの鍵を握った。 A using In honeypots .
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a と Pros Cons を用いた、ハニーポットのランダムな森林評価に基づく、ハニーポットの社会的な敵の収集は、26 倍の速さで改善された。 OSN のメアに主に依存する誘惑は、制限のある新しい用語で、より多く、行動的に収集される。
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ネットワークは悪質な身元を4人のベイジアンc4.5に結びつけ、プレト・ナ・シュヴェ=ベイズと(dt)データにテーブルを割り当てる。 訳抜け防止モード: ネットワークは悪質な身元を ベイジアンc4.5に つなげる preto na sıve - bayes and (dt )、table to data を含む。
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ネットワーク a を [114] に分析する: ターゲットはノードを攻撃でき、プロトコルや実行中の os 特有のネットワーク機能を利用することができる。 訳抜け防止モード: network a analyzeTo [114 ] : ターゲットがノードを攻撃できる。 ターゲットの使用 プロトコルや実行中のOSに特有のネットワーク機能には、ツールの指紋偽装などがある。
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偽の損失を広めるために[37]、the withの認可(例えば、機密と可用性への攻撃)が可能です。
0.71
Impersonation attacks ade legitimate user’s system component attack Jamming seen be as a that jamming wireless in The archive.
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名前は「jamer」で ワイヤレスの パフォーマンスを犠牲にしてます
0.51
confidentiality, of An 51]: insider 50, confidential leak out Further, legitimate user.
秘密性、An 51]:インサイダー50、秘密漏洩、さらに正当なユーザー。
0.59
of a access, illegal information)
アクセスについて)違法な情報
0.73
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悪質な雇用主や デートのように利己的だ
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0.70
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0.67
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すべて は あ 攻撃:身元
0.60
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(a.k.a.) 数ベース物理
0.56
Sybil fake of on others’ addresses,
Sybilは他人の住所を偽造する。
0.64
OSN to personal date of
OSN (複数形 OSNs)
0.59
attackers, photos. [21]: Human disseminate
攻撃者 写真だ 〔21〕人散布
0.61
goal e-mail, or by paid crowdturfing information false to can purposes via crowdsourcemployers’ This mostly and called crowdturfing information to mislead people’s beliefs.
メールをゴールにするか、あるいはクラウドソース・プルーパーズ(クラウドソース・プルーパーズ)を通じて、偽情報を偽造するかで、クラウドソーシングの情報を主に呼び出して人々の信念を誤解させる。 訳抜け防止モード: goal e - mail, or by paid crowdturfing information false to can purpose through crowdsourcemployers& quot; これは主に、人々の信念を誤解させるために、クラウドサーフィン情報と呼ばれる。
0.67
(CIA) availability and integrity, [120], a called attacker, traitor as to information the status legitimate its by using it device, target perform can transformation modification or integrity loss to lead that
attacks. on focus DoS attacks attack is packets, effectively disrupt even and
攻撃だ 集中DoS攻撃はパケットであり、効果的に破壊する。
0.72
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ジャミング インカリングはブロックが通常の損傷を比較的妨害する可能性がある
0.73
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can攻撃は、攻撃者、コミュニカ操作、制御に適用されるトラフィックの違いである。
0.58
英語（論文から抽出）
日本語訳
スコア
• • system [66].
• • システム [66]
0.78
device Node or 65, 63, 58, 73, 87, research does not Some authors only use The research attack.
device Node, 65, 63, 58, 73, 87, research does not 一部の著者は研究攻撃のみを使用する。
0.84
Some [2, a target attacking before 141] 135, 100, 87, 111, 99, 24, actions attacking [23, 30, attacker [61]: An Web attack bilities applications to in web the web access unauthorized 3
いくつかの[2, a target attacking before 141] 135, 100, 87, 111, 99, 24] アクション攻撃 [23, 30] アタッカー [61]: Web で Web アクセスを許可されていない Web アクセスに Web 攻撃能力を持つアプリケーション。
0.87
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[2, 3, 12, 13, 15, 15 14 compromise 81, 99, 100, 104, 111, 106, attack of details the specific [device attacker an discuss that 13, 14, 15, 3, 12, only other while 106]
0.80
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73, 58, 104, 既存のデータサーバを活用できる。
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sensitive to 24, 23, 30, 141]: 135, process.
敏感 へ 24, 23, 30, 141]: 135, process.
0.72
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3. learning In Types and frequency of attacks defensive deception approaches.
3.学習 攻撃のタイプと頻度は防御的詐欺に近づく。
0.71
of gameFigs.
GameFigsの略。
0.66
2 and 3, we summarized techniques defensive or ML-based theoretic type that paper.
2および3では, 防御的, MLに基づく理論的手法を要約した。
0.55
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0.54
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偵察) フィギュア。
0.38
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0.35
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0.43
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ノードが2つずつ 主に
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detect 3, VII.
検出 3, VII。
0.78
APPLICATION DOMAINS
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0.47
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0.25
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戦略だ 一般的な防御策にはファイルが含まれます
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高度な[139, アタックアタック[40]による攻撃。
0.75
game-theoretic environment, [156]
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0.70
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ハニーフェイク情報、タイプ156のハニーのような使用技術で環境ネットワークを防御します。
0.57
106, 111, honeypots or 139] enterprise networks, Attacks: Main 2) game-theoretic countered by been have attacks threat and insider including based DD, [73], zero-day APTs versaries, such as engineering reverse worm attacks [32], [19], or DoS Spafford patch Avery and DD For Methods: ML GT and Key signalnetwork this in deployed techniques have Stackelberg 106] games ing or [40, 104, as game Some commonly been used.
106, 111, honeypots or 139] enterprise network, Attacks: Main 2) game-theoretic countered by were have attack threat and insider including base DD, [73], zero-day APTs versaries, such as engineering reverse worm attack [32], [19], or DoS Spafford patch Avery and DD For Methods: ML GT and Key signalnetwork this in deployment techniques have Stackelberg 106] games ing or [40, 104, as game ] ゲームとしてよく使われている。
0.88
different usBayesian or [111] Utility Metagames/Expected to also Bayesian have [40] ing been MLcontext.
異なる usBayesian または [111] Utility Metagames/Expected to Bayesian have [40] ing は MLcontext です。
0.92
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DDでは、このネットワーク(NLP)処理言語DDベースのNaturalファイルを開発する。
0.82
honey develop to used have techniques been DD apMajority of game-theoretic Pros providproaches has enterprise network without the aim of examining ing the details with solid theoretical proposed a solutions strategies or analysis, gamehighly based theoretical concrete theoretic DD approaches may provide on how to design DD techniques considering the characteristics of a availability, given system security and performance/services requirements, or netaddition, some general work specify particular game-theoretic DD modeling idea techniques, a of but limited details, technique.
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0.91
DD showing networks applicability in enterprise are highly complex systems which may need mostly deal with to a gamewide range of networks, theoretic don’t they types of attacks.
tion software-defined (IoT), of Things systems control dustrial in networks these to particularly discuss for designed ‘general platform.
tion software-defined (iot) of things systems control dustrial in networks これらは特に‘general platform’として設計されている。
0.77
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CPSの存在とそのネットワーク(SDN)は、このテクニックを議論している。
0.74
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テクニック: デプロイは環境に制限はない。
0.75
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DDにおけるProsの開発環境は、CPSサイバーと特性に特有の多様なCPSを開発した。
0.70
environment physical between differences Hence, DD techniques.
環境 物理的に違いがあるため、DD技術。
0.63
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そのようなハニーポットの展開とメンテナンスがしばしば高い展開をもたらすときの推奨
0.66
signaling A Methods: and ML optimal an to defender identify select to addition, In [121].
signaling A Methods: and ML optimal an to defender identify select to addition, In[121]。
0.76
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0.70
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2) niques web attacks 3) plete, defense rithms 4) posed based privacy defend。 訳抜け防止モード: 2 ) niques web attacks 3 ) plete, defense rithms 4 ) posed based privacy defend しかし、 例を挙げる。
0.78
features of a given DD technique only for environment.
特定のDD技術の特徴は環境のみである。
0.76
DD techgeneral considered have mainly [105], or privacy attacks [152].
DDの技術者は、主に[105]、またはプライバシー攻撃(152]とみなした。
0.70
classifiers of ML-based accuracy comgames with Signaling and ML Methods: attackbeen have information to model used addition, ML-based In [61, 152].
Signaling and ML Methods: attackbeenは、MLベースのIn[61, 152]をモデル化するための情報を持っている。
0.68
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0.45
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またはAPT攻撃 [87, incomgames with Bayesian Key GT and ML Methods: 3) and meta information player a are plete for consider of type [87] is a unsure of who [87]。
0.80
A signaling attackers game is considered with a perfect Bayesian equilibrium to model also and attacker an between interactions IoTs the defender where IoBTs to referred is battlefields in games 141] 23, 13, in [14, 15, 12, 99, introduced fake nodes mimSince honeypots are and Cons: Pros honeypot won’t adding a regular node, a behavior of icking the of interface or network change the the hierarchy of the technique IoT is honeypot the IoT domain.
シグナリングアタッカーズゲーム(signaling attacks game)は、モデリングのための完全なベイズ的均衡とアタック an インタラクション iots ディフェンダー iobts がゲーム内のバトルフィールド 141] 23 13 in [14, 15, 12, 99, introduced fake node mim since honeypots are and cons: pros honeypot's not addition a regular node, a behavior of the imcking the interface or network change iot テクニックの階層構造は iot ドメインハニーポット(honeypot the iot domain)である。
0.69
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しかし、ML ベースのデータに対して DD real-like deoys は検出を強化します。
0.64
IoT game-theoretic A to applied this large a generate create techniques which can outside and attackers.
IoTゲーム理論Aを適用することで、外部とアタッカーに可能なテクニックを生成することができる。
0.62
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0.75
(SDNs) SDN data forwarding) from control-plane [92].
(SDN) コントロールプレーンからのSDNデータ転送 [92]。
0.73
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ネットワーク OpenFlowとスイッチ
0.68
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0.50
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0.82
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0.57
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DDテクニックでは、リフレクションCPSは環境に固有の課題を提起しません。
0.70
CPS honeypot Physical deployment and maintenance often come with higher deployment and management cost.
CPS ハニーポット 物理的配置とメンテナンスは、しばしばより高い配置と管理コストをもたらす。
0.62
designs Some detailed should be considered to unique deal with challenges of cloud
クラウドの課題に独特な対処法として考慮すべき詳細設計
0.76
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環境。 はあ? ハニーポットは
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ゲーム理論は、その技法領域である。
0.55
this to the However, IoT a can generate naturally large for data of amount ML-based DD techniques decoys which or real that inside enhance and attackers.
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既存のsingle a useの他には、single point of controllerがある。
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create can looks like detect
createは「検出」のように見える
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企業環境へのアプローチ
0.75
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0.92
reconnaissance target towards a port IP numbers) network mapping, and Methods: and ML under adopted various incomplete information, equilibrium.
[55], munication capture network packets control and systems information protect and other systems compromised from potentially to is deception Defensive transmit used jamming over fake mitigate to channels [101].
networks Game Attacks: Main 2) defended have niques jamming against attacks core networks tihop wireless 3G and network compromise servers or gateways that support the mobile packet in services radio general A non-cooperative, non-zeroKey GT and ML Methods: interactions an to model the routing [55].
ネットワーク攻撃: メイン2 防衛対象は、無線3Gとネットワーク妥協サーバ、あるいはサービスラジオのモバイルパケットをサポートするゲートウェイに対して、攻撃を妨害するニケを犯している 一般 非協力的で、非ゼロキーのGTおよびMLメソッド: ルーティングをモデル化するための相互作用 [55]。 訳抜け防止モード: ネットワークゲームアタック : main 2 ) defended have niques jamming against attack network tihop wireless 3g and network compromise server or gateways that support the mobile packet in services radio general a non- cooperative, non-zerokey gt そして、mlメソッド:ルーティングをモデル化するためのインタラクション [55 ]。
0.84
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ディフェンダー無線による対局送信を利用した欺きゲーム
0.60
proposed is flow in multihop packets address moarchitecture to developed was [55] telecomof mobile network designed was gateway to investigate as as well launched from attacks the inside honeynet.
提案するis flow in multihop packets address moarchitecture to developed was [55] telecomof mobile network designed was gateway to investigation, and launch from attack the inside honeynet
0.79
information fake attacks in wireless
無線における情報偽装攻撃
0.74
techDD or ML-based in [49] mulwhich attacks nodes providing [55].
techDDまたはMLベースの [49] mul whichは[55]を提供するノードを攻撃します。
0.64
networks between paths are Stackelberg allocation networks
ネットワーク 経路間の経路はstackelbergアロケーションネットワークである
0.67
two-stage power to
two‐stage power
0.88
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0.58
sum static game and attacker a designed based a theory [49].
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0.69
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認知はゲーム適用理論によるジャミング[101]攻撃である。
0.71
and 4) Pros Cons: key concerns of wireless nications real However,
4) 長所: ワイヤレスのニシエーションに関する重要な懸念は、しかし、
0.69
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システムベースで モバイル・ワイヤレスなら
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on [55], デザインは
0.62
deal with Specific communetworks, security, implement game-theoretic DD technologies.
特定のコミュートワーク、セキュリティ、ゲーム理論DD技術の実装を扱う。
0.64
heavily honeynet a
ヘビーハニーネットA
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0.77
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MLに基づくゲームの有効性について
0.61
or domains, Table V.
またはドメイン、テーブルv。
0.76
the the accuracy framework under convenience
はあ? 便宜上の正確さの枠組み
0.51
of may different easy of
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0.49
DD summarized techniques our
DD 要約 我々の技術は
0.63
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同じです はあ? ハニーポット
0.44
deployed not guarantee deployment settings.
デプロイ設定が保証されない。
0.59
for look-up different on this
ルックアップでこれと違うのは
0.50
based discussions of
ベースの議論 ですから
0.58
game-theoretic application section in
ゲーム理論の適用セクション
0.69
20 forlevel
20 forlevel
0.85
VIII. EVALUATION OF DEFENSIVE DECEPTION
VIII。 評価 防御的デセプションの
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0.82
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Most ML-based approaches were mainly studied detection attack achieve to using which as accuracy a utility and effectiveness detection accuracy, and core merit of using Evaluation els.
testbeds based are mostly some ML-based DD are mostly social platforms.
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0.58
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0.73
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0.48
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We attack behaviors performing multi-staged attackers accommodate that where a to defender needs take more defense tactics, intent, attacker’s on utilities.
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based on techniques deception
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0.69
by deception the extent metric may attacker
偽りによって メトリックは攻撃するかもしれない
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intelligent and deployed defensive of DD.
知的で 防御を施し DD。
0.56
A metric to measure
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0.73
consider game both
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atdetect to deception variety of extend attack models attacks based
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promising honey-X techof assessment security their despite honey-X (as concepts discussed assessment security effectiveness of honey-
literature. the observed have not ML game theory and own estimate their need to defenders and attackers Since ML-based belief to take, strategy what beliefs to decide view towards accurate its player’s a provide can estimation when addition, In will the take.
strategy it and opponent take to approach game-theoretic an optimal use players a of DD techniques can be also developed using strategy, a set the by the of quality increase to ML technologies or objects real mimicking information.
itと対戦相手がゲーム理論にアプローチする戦略 dd技術の最適な使用者 a は、ml技術や実際の模倣情報に対する品質向上によって設定される戦略を用いて開発することもできる。
0.80
more Need measure to metrics and efficiency of DD techniques: As we discussed in Section IX-B, studies game-theoretic most utility used and probabilwhile ities of ML-based DD approaches the major metric.
第IX-B節で議論したように、MLベースのDDのゲーム理論で最も有用で確率的な性質の研究は主要なメトリクスに近づきます。 訳抜け防止モード: dd技術のメトリクスと効率にもっと必要なもの : 第9x-b節で論じたように,研究ゲーム - ml の最も実用的かつ確率的な性質- は,dd が主要な指標にアプローチする。
0.62
Using accuracy mainly as meaningful metrics to measure the actual and can critical are deception defensive efficiency we on provide justifications why want introduce defense other over proactive DD approaches Need emulation, or real evaluation deception defensive game-theoretic studied been have approaches theoretic models.
The views and conclusions the are of as authors not should and or implied, representing the official expressed either policies, or Laboratory Research of Government.
Army U.S. the reproduce and distribute authorized to The U.S. Government is Government reprints purposes copyright herein.
アメリカ陸軍 アメリカ合衆国政府に認可された複製と配布は、政府による著作権のリプリントである。
0.66
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