Evidential Cyber Threat Hunting
- URL: http://arxiv.org/abs/2104.10319v1
- Date: Wed, 21 Apr 2021 02:38:29 GMT
- Title: Evidential Cyber Threat Hunting
- Authors: Frederico Araujo and Dhilung Kirat and Xiaokui Shu and Teryl Taylor
and Jiyong Jang
- Abstract summary: A formal cyber reasoning framework for automating the threat hunting process is described.
The new cyber reasoning methodology introduces an operational semantics that operates over three subspaces.
An implementation of this framework shows that the approach is practical and can be used to generalize evidence-based multi-criteria threat investigations.
- Score: 4.1535961847899925
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A formal cyber reasoning framework for automating the threat hunting process
is described. The new cyber reasoning methodology introduces an operational
semantics that operates over three subspaces -- knowledge, hypothesis, and
action -- to enable human-machine co-creation of threat hypotheses and
protective recommendations. An implementation of this framework shows that the
approach is practical and can be used to generalize evidence-based
multi-criteria threat investigations.
Related papers
- Threat-Informed Cyber Resilience Index: A Probabilistic Quantitative Approach to Measure Defence Effectiveness Against Cyber Attacks [0.36832029288386137]
This paper introduces the Cyber Resilience Index (CRI), a threat-informed probabilistic approach to quantifying an organisation's defence effectiveness against cyber-attacks (campaigns)
Building upon the Threat-Intelligence Based Security Assessment (TIBSA) methodology, we present a mathematical model that translates complex threat intelligence into an actionable, unified metric similar to a stock market index, that executives can understand and interact with while teams can act upon.
arXiv Detail & Related papers (2024-06-27T17:51:48Z) - Siren -- Advancing Cybersecurity through Deception and Adaptive Analysis [0.0]
This project employs sophisticated methods to lure potential threats into controlled environments.
The architectural framework includes a link monitoring proxy, a purpose-built machine learning model for dynamic link analysis.
The incorporation of simulated user activity extends the system's capacity to capture and learn from potential attackers.
arXiv Detail & Related papers (2024-06-10T12:47:49Z) - Asset-centric Threat Modeling for AI-based Systems [7.696807063718328]
This paper presents ThreatFinderAI, an approach and tool to model AI-related assets, threats, countermeasures, and quantify residual risks.
To evaluate the practicality of the approach, participants were tasked to recreate a threat model developed by cybersecurity experts of an AI-based healthcare platform.
Overall, the solution's usability was well-perceived and effectively supports threat identification and risk discussion.
arXiv Detail & Related papers (2024-03-11T08:40:01Z) - Towards more Practical Threat Models in Artificial Intelligence Security [66.67624011455423]
Recent works have identified a gap between research and practice in artificial intelligence security.
We revisit the threat models of the six most studied attacks in AI security research and match them to AI usage in practice.
arXiv Detail & Related papers (2023-11-16T16:09:44Z) - Threat Trekker: An Approach to Cyber Threat Hunting [0.5371337604556311]
Threat hunting is a proactive methodology for exploring, detecting and mitigating cyberattacks.
This paper introduces a novel machine learning paradigm known as Threat Trekker.
arXiv Detail & Related papers (2023-10-06T12:29:41Z) - On the Security Risks of Knowledge Graph Reasoning [71.64027889145261]
We systematize the security threats to KGR according to the adversary's objectives, knowledge, and attack vectors.
We present ROAR, a new class of attacks that instantiate a variety of such threats.
We explore potential countermeasures against ROAR, including filtering of potentially poisoning knowledge and training with adversarially augmented queries.
arXiv Detail & Related papers (2023-05-03T18:47:42Z) - A System for Efficiently Hunting for Cyber Threats in Computer Systems
Using Threat Intelligence [78.23170229258162]
We build ThreatRaptor, a system that facilitates cyber threat hunting in computer systems using OSCTI.
ThreatRaptor provides (1) an unsupervised, light-weight, and accurate NLP pipeline that extracts structured threat behaviors from unstructured OSCTI text, (2) a concise and expressive domain-specific query language, TBQL, to hunt for malicious system activities, and (3) a query synthesis mechanism that automatically synthesizes a TBQL query from the extracted threat behaviors.
arXiv Detail & Related papers (2021-01-17T19:44:09Z) - Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence [94.94833077653998]
ThreatRaptor is a system that facilitates threat hunting in computer systems using open-source Cyber Threat Intelligence (OSCTI)
It extracts structured threat behaviors from unstructured OSCTI text and uses a concise and expressive domain-specific query language, TBQL, to hunt for malicious system activities.
Evaluations on a broad set of attack cases demonstrate the accuracy and efficiency of ThreatRaptor in practical threat hunting.
arXiv Detail & Related papers (2020-10-26T14:54:01Z) - Risk-Sensitive Sequential Action Control with Multi-Modal Human
Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure.
Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control.
A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
arXiv Detail & Related papers (2020-09-12T02:02:52Z) - Adversarial Machine Learning Attacks and Defense Methods in the Cyber
Security Domain [58.30296637276011]
This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques.
It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
arXiv Detail & Related papers (2020-07-05T18:22:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.