Use of Graph Neural Networks in Aiding Defensive Cyber Operations

• URL: http://arxiv.org/abs/2401.05680v1
• Date: Thu, 11 Jan 2024 05:56:29 GMT
• Title: Use of Graph Neural Networks in Aiding Defensive Cyber Operations
• Authors: Shaswata Mitra, Trisha Chakraborty, Subash Neupane, Aritran Piplai, Sudip Mittal
• Abstract summary: Graph Neural Networks have emerged as a promising approach for enhancing the effectiveness of defensive measures. We look into the application of GNNs in aiding to break each stage of one of the most renowned attack life cycles, the Lockheed Martin Cyber Kill Chain.
• Score: 2.1874189959020427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In an increasingly interconnected world, where information is the lifeblood of modern society, regular cyber-attacks sabotage the confidentiality, integrity, and availability of digital systems and information. Additionally, cyber-attacks differ depending on the objective and evolve rapidly to disguise defensive systems. However, a typical cyber-attack demonstrates a series of stages from attack initiation to final resolution, called an attack life cycle. These diverse characteristics and the relentless evolution of cyber attacks have led cyber defense to adopt modern approaches like Machine Learning to bolster defensive measures and break the attack life cycle. Among the adopted ML approaches, Graph Neural Networks have emerged as a promising approach for enhancing the effectiveness of defensive measures due to their ability to process and learn from heterogeneous cyber threat data. In this paper, we look into the application of GNNs in aiding to break each stage of one of the most renowned attack life cycles, the Lockheed Martin Cyber Kill Chain. We address each phase of CKC and discuss how GNNs contribute to preparing and preventing an attack from a defensive standpoint. Furthermore, We also discuss open research areas and further improvement scopes.

Related papers

• From Sands to Mansions: Enabling Automatic Full-Life-Cycle Cyberattack Construction with LLM [6.534605400247412]
  Existing cyberattack simulation frameworks face challenges such as limited technical coverage, inability to conduct full-life-cycle attacks, and the need for manual infrastructure building. We proposed AURORA, an automatic end-to-end cyberattack construction and emulation framework. AURORA can autonomously build multi-stage cyberattack plans based on Cyber Threat Intelligence (CTI) reports, construct the emulation infrastructures, and execute the attack procedures.
  arXiv  Detail & Related papers  (2024-07-24T01:33:57Z)
• Towards in-situ Psychological Profiling of Cybercriminals Using Dynamically Generated Deception Environments [0.0]
  Cybercrime is estimated to cost the global economy almost $10 trillion annually. Traditional perimeter security approach to cyber defence has so far proved inadequate to combat the growing threat of cybercrime. Deceptive techniques aim to mislead attackers, diverting them from critical assets whilst simultaneously gathering cyber threat intelligence on the threat actor. This article presents a proof-of-concept system that has been developed to capture the profile of an attacker in-situ, during a simulated cyber-attack in real time.
  arXiv  Detail & Related papers  (2024-05-19T09:48:59Z)
• Graph Mining for Cybersecurity: A Survey [61.505995908021525]
  The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
  arXiv  Detail & Related papers  (2023-04-02T08:43:03Z)
• Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS [70.60975663021952]
  We study blackbox adversarial attacks on network classifiers. We argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions. We show that a continual learning approach is required to study attacker-defender dynamics.
  arXiv  Detail & Related papers  (2021-11-23T23:42:16Z)
• Reinforcement Learning for Feedback-Enabled Cyber Resilience [24.92055101652206]
  Cyber resilience provides a new security paradigm that complements inadequate protection with resilience mechanisms. A Cyber-Resilient Mechanism ( CRM) adapts to the known or zero-day threats and uncertainties in real-time. We review the literature on RL for cyber resiliency and discuss the cyber-resilient defenses against three major types of vulnerabilities.
  arXiv  Detail & Related papers  (2021-07-02T01:08:45Z)
• The Feasibility and Inevitability of Stealth Attacks [63.14766152741211]
  We study new adversarial perturbations that enable an attacker to gain control over decisions in generic Artificial Intelligence systems. In contrast to adversarial data modification, the attack mechanism we consider here involves alterations to the AI system itself.
  arXiv  Detail & Related papers  (2021-06-26T10:50:07Z)
• Online Alternate Generator against Adversarial Attacks [144.45529828523408]
  Deep learning models are notoriously sensitive to adversarial examples which are synthesized by adding quasi-perceptible noises on real images. We propose a portable defense method, online alternate generator, which does not need to access or modify the parameters of the target networks. The proposed method works by online synthesizing another image from scratch for an input image, instead of removing or destroying adversarial noises.
  arXiv  Detail & Related papers  (2020-09-17T07:11:16Z)
• Measurement-driven Security Analysis of Imperceptible Impersonation Attacks [54.727945432381716]
  We study the exploitability of Deep Neural Network-based Face Recognition systems. We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
  arXiv  Detail & Related papers  (2020-08-26T19:27:27Z)
• 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.