SAGE: Intrusion Alert-driven Attack Graph Extractor

• URL: http://arxiv.org/abs/2107.02783v1
• Date: Tue, 6 Jul 2021 17:45:02 GMT
• Title: SAGE: Intrusion Alert-driven Attack Graph Extractor
• Authors: Azqa Nadeem, Sicco Verwer, Stephen Moskal, Shanchieh Jay Yang
• Abstract summary: Attack graphs (AGs) are used to assess pathways availed by cyber adversaries to penetrate a network. We propose to automatically learn AGs based on actions observed through intrusion alerts, without prior expert knowledge.
• Score: 4.530678016396476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Attack graphs (AG) are used to assess pathways availed by cyber adversaries to penetrate a network. State-of-the-art approaches for AG generation focus mostly on deriving dependencies between system vulnerabilities based on network scans and expert knowledge. In real-world operations however, it is costly and ineffective to rely on constant vulnerability scanning and expert-crafted AGs. We propose to automatically learn AGs based on actions observed through intrusion alerts, without prior expert knowledge. Specifically, we develop an unsupervised sequence learning system, SAGE, that leverages the temporal and probabilistic dependence between alerts in a suffix-based probabilistic deterministic finite automaton (S-PDFA) -- a model that accentuates infrequent severe alerts and summarizes paths leading to them. AGs are then derived from the S-PDFA. Tested with intrusion alerts collected through Collegiate Penetration Testing Competition, SAGE produces AGs that reflect the strategies used by participating teams. The resulting AGs are succinct, interpretable, and enable analysts to derive actionable insights, e.g., attackers tend to follow shorter paths after they have discovered a longer one.

Related papers

• Preliminary Investigation into Uncertainty-Aware Attack Stage Classification [81.28215542218724]
  This work addresses the problem of attack stage inference under uncertainty.<n>We propose a classification approach based on Evidential Deep Learning (EDL), which models predictive uncertainty by outputting parameters of a Dirichlet distribution over possible stages.<n>Preliminary experiments in a simulated environment demonstrate that the proposed model can accurately infer the stage of an attack with confidence.
  arXiv  Detail & Related papers  (2025-08-01T06:58:00Z)
• CyberRAG: An agentic RAG cyber attack classification and reporting tool [1.0345929832241807]
  CyberRAG is a modular, agent-based framework that delivers real-time classification, explanation, and structured reporting for cyber-attacks.<n>Unlike traditional RAG systems, CyberRAG embraces an agentic design that enables dynamic control flow and adaptive reasoning.<n>CyberRAG has been evaluated achieving over 94% accuracy per class and pushing final classification accuracy to 94.92%.
  arXiv  Detail & Related papers  (2025-07-03T08:32:19Z)
• The Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems [101.68501850486179]
  We explore adversarial attacks against retrieval-augmented generation (RAG) systems to identify their vulnerabilities.<n>This task aims to find imperceptible perturbations that retrieve a target document, originally excluded from the initial top-$k$ candidate set.<n>We propose ReGENT, a reinforcement learning-based framework that tracks interactions between the attacker and the target RAG.
  arXiv  Detail & Related papers  (2025-05-24T08:19:25Z)
• OMNISEC: LLM-Driven Provenance-based Intrusion Detection via Retrieval-Augmented Behavior Prompting [4.71781133841068]
  Provenance-based Intrusion Detection Systems (PIDSes) have been widely used for endpoint threat analysis.<n>Due to the evolution of attack techniques, rules cannot dynamically model all the characteristics of attackers.<n>Anomaly-based detection systems face a massive false positive problem because they cannot distinguish between changes in normal behavior and real attack behavior.
  arXiv  Detail & Related papers  (2025-03-05T02:08:12Z)
• Forecasting Attacker Actions using Alert-driven Attack Graphs [1.3812010983144802]
  This paper builds an action forecasting capability on top of the alert-driven AG framework for predicting the next likely attacker action. We also modify the framework to build AGs in real time, as new alerts are triggered. This way, we convert alert-driven AGs into an early warning system that enables analysts circumvent ongoing attacks and break the cyber killchain.
  arXiv  Detail & Related papers  (2024-08-19T11:04:47Z)
• HADES: Detecting Active Directory Attacks via Whole Network Provenance Analytics [7.203330561731627]
  Active Directory (AD) is a top target of Advanced Persistence Threat (APT) actors. We propose HADES, the first PIDS capable of performing accurate causality-based cross-machine tracing. We introduce a novel lightweight authentication anomaly detection model rooted in our analysis of AD attacks.
  arXiv  Detail & Related papers  (2024-07-26T16:46:29Z)
• Pre-trained Trojan Attacks for Visual Recognition [106.13792185398863]
  Pre-trained vision models (PVMs) have become a dominant component due to their exceptional performance when fine-tuned for downstream tasks. We propose the Pre-trained Trojan attack, which embeds backdoors into a PVM, enabling attacks across various downstream vision tasks. We highlight the challenges posed by cross-task activation and shortcut connections in successful backdoor attacks.
  arXiv  Detail & Related papers  (2023-12-23T05:51:40Z)
• 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)
• Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
  We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers. Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods. Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
  arXiv  Detail & Related papers  (2022-07-20T19:49:09Z)
• On Trace of PGD-Like Adversarial Attacks [77.75152218980605]
  Adversarial attacks pose safety and security concerns for deep learning applications. We construct Adrial Response Characteristics (ARC) features to reflect the model's gradient consistency. Our method is intuitive, light-weighted, non-intrusive, and data-undemanding.
  arXiv  Detail & Related papers  (2022-05-19T14:26:50Z)
• Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection [4.56877715768796]
  We propose the adoption of meta-learning, in the form of a two-layer Stacker, to create a mixed approach that detects both known and unknown threats. It turns out to be more effective in detecting zero-day attacks than supervised algorithms, limiting their main weakness but still maintaining adequate capabilities in detecting known attacks.
  arXiv  Detail & Related papers  (2022-02-28T08:41:32Z)
• Early Detection of Network Attacks Using Deep Learning [0.0]
  A network intrusion detection system (IDS) is a tool used for identifying unauthorized and malicious behavior by observing the network traffic. We propose an end-to-end early intrusion detection system to prevent network attacks before they could cause any more damage to the system under attack.
  arXiv  Detail & Related papers  (2022-01-27T16:35:37Z)
• A Rule Mining-Based Advanced Persistent Threats Detection System [2.75264806444313]
  Advanced persistent threats (APT) are stealthy cyber-attacks aimed at stealing valuable information from target organizations. Provenance-tracking and trace mining are considered promising as they can help find causal relationships between activities and flag suspicious event sequences as they occur. We introduce an unsupervised method that exploits OS-independent features reflecting process activity to detect realistic APT-like attacks from provenance traces.
  arXiv  Detail & Related papers  (2021-05-20T22:13:13Z)
• Graph Backdoor [53.70971502299977]
  We present GTA, the first backdoor attack on graph neural networks (GNNs) GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features. It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
  arXiv  Detail & Related papers  (2020-06-21T19:45:30Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

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.