Enabling Efficient Attack Investigation via Human-in-the-Loop Security Analysis

• URL: http://arxiv.org/abs/2211.05403v2
• Date: Tue, 03 Dec 2024 05:18:59 GMT
• Title: Enabling Efficient Attack Investigation via Human-in-the-Loop Security Analysis
• Authors: Xinyu Yang, Haoyuan Liu, Saimon Amanuel Tsegai, Peng Gao,
• Abstract summary: Raptor is a defense system that enables human analysts to effectively analyze large-scale system provenance. ProvQL offers essential primitives for various types of attack analyses. Raptor provides an optimized execution engine for efficient language execution.
• Score: 19.805667450941403
• License:
• Abstract: System auditing is a vital technique for collecting system call events as system provenance and investigating complex multi-step attacks such as Advanced Persistent Threats. However, existing attack investigation methods struggle to uncover long attack sequences due to the massive volume of system provenance data and their inability to focus on attack-relevant parts. In this paper, we present Raptor, a defense system that enables human analysts to effectively analyze large-scale system provenance to reveal multi-step attack sequences. Raptor introduces an expressive domain-specific language, ProvQL, that offers essential primitives for various types of attack analyses (e.g., attack pattern search, attack dependency tracking) with user-defined constraints, enabling analysts to focus on attack-relevant parts and iteratively sift through the large provenance data. Moreover, Raptor provides an optimized execution engine for efficient language execution. Our extensive evaluations on a wide range of attack scenarios demonstrate the practical effectiveness of Raptor in facilitating timely attack investigation.

Related papers

• Exploring Answer Set Programming for Provenance Graph-Based Cyber Threat Detection: A Novel Approach [4.302577059401172]
  Provenance graphs are useful tools for representing system-level activities in cybersecurity. This paper presents a novel approach using ASP to model and analyze provenance graphs.
  arXiv  Detail & Related papers  (2025-01-24T14:57:27Z)
• HijackRAG: Hijacking Attacks against Retrieval-Augmented Large Language Models [18.301965456681764]
  We reveal a novel vulnerability, the retrieval prompt hijack attack (HijackRAG) HijackRAG enables attackers to manipulate the retrieval mechanisms of RAG systems by injecting malicious texts into the knowledge database. We propose both black-box and white-box attack strategies tailored to different levels of the attacker's knowledge.
  arXiv  Detail & Related papers  (2024-10-30T09:15:51Z)
• Slot: Provenance-Driven APT Detection through Graph Reinforcement Learning [24.84110719035862]
  Advanced Persistent Threats (APTs) represent sophisticated cyberattacks characterized by their ability to remain undetected for extended periods. We propose Slot, an advanced APT detection approach based on provenance graphs and graph reinforcement learning. We show Slot's outstanding accuracy, efficiency, adaptability, and robustness in APT detection, with most metrics surpassing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-23T14:28:32Z)
• From Sands to Mansions: Simulating Full Attack Chain with LLM-Organized Knowledge [10.065241604400223]
  Multi-stage attack simulations offer a promising approach to enhance system evaluation efficiency. simulating a full attack chain is complex and requires significant time and expertise from security professionals. We introduce Aurora, a system that autonomously simulates full attack chains based on external attack tools and threat intelligence reports.
  arXiv  Detail & Related papers  (2024-07-24T01:33:57Z)
• Corpus Poisoning via Approximate Greedy Gradient Descent [48.5847914481222]
  We propose Approximate Greedy Gradient Descent, a new attack on dense retrieval systems based on the widely used HotFlip method for generating adversarial passages. We show that our method achieves a high attack success rate on several datasets and using several retrievers, and can generalize to unseen queries and new domains.
  arXiv  Detail & Related papers  (2024-06-07T17:02:35Z)
• It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation [50.06412862964449]
  Attack Graph (AG) represents the best-suited solution to support cyber risk assessment for multi-step attacks on computer networks. Current solutions propose to address the generation problem from the algorithmic perspective and postulate the analysis only after the generation is complete. This paper rethinks the classic AG analysis through a novel workflow in which the analyst can query the system anytime.
  arXiv  Detail & Related papers  (2023-12-27T10:44:58Z)
• Streamlining Attack Tree Generation: A Fragment-Based Approach [39.157069600312774]
  We present a novel fragment-based attack graph generation approach that utilizes information from publicly available information security databases. We also propose a domain-specific language for attack modeling, which we employ in the proposed attack graph generation approach.
  arXiv  Detail & Related papers  (2023-10-01T12:41:38Z)
• 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)
• Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems [50.758281304737444]
  We propose to explore adversarial examples and attack detection on reinforcement learning-based interactive recommendation systems. We first craft different types of adversarial examples by adding perturbations to the input and intervening on the casual factors. Then, we augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data.
  arXiv  Detail & Related papers  (2021-12-02T04:12:24Z)
• 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)
• Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification [78.51092318750102]
  This work proposes to defend ASV systems against adversarial attacks with a separate detection network. A VGG-like binary classification detector is introduced and demonstrated to be effective on detecting adversarial samples.
  arXiv  Detail & Related papers  (2020-06-11T04:31:56Z)

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.