[论文解读] Impacts and Risk of Generative AI Technology on Cyber Defense

Subash Neupane, Ivan Fernandez+2 more|arXiv (Cornell University)|Jun 22, 2023

Advanced Malware Detection Techniques被引用 13

一句话总结

本文分析了进攻性生成式人工智能（GenAI）在网络攻击生命周期每个阶段的影响，并提出基于检测、欺骗和对抗性训练的面向攻击的自适应防御。

ABSTRACT

Generative Artificial Intelligence (GenAI) has emerged as a powerful technology capable of autonomously producing highly realistic content in various domains, such as text, images, audio, and videos. With its potential for positive applications in creative arts, content generation, virtual assistants, and data synthesis, GenAI has garnered significant attention and adoption. However, the increasing adoption of GenAI raises concerns about its potential misuse for crafting convincing phishing emails, generating disinformation through deepfake videos, and spreading misinformation via authentic-looking social media posts, posing a new set of challenges and risks in the realm of cybersecurity. To combat the threats posed by GenAI, we propose leveraging the Cyber Kill Chain (CKC) to understand the lifecycle of cyberattacks, as a foundational model for cyber defense. This paper aims to provide a comprehensive analysis of the risk areas introduced by the offensive use of GenAI techniques in each phase of the CKC framework. We also analyze the strategies employed by threat actors and examine their utilization throughout different phases of the CKC, highlighting the implications for cyber defense. Additionally, we propose GenAI-enabled defense strategies that are both attack-aware and adaptive. These strategies encompass various techniques such as detection, deception, and adversarial training, among others, aiming to effectively mitigate the risks posed by GenAI-induced cyber threats.

研究动机与目标

• 识别进攻性 GenAI 如何影响 Cyber Kill Chain（CKC）的每个阶段。
• 探索威胁行为者利用 GenAI 进行网络攻击的策略。
• 提出一套防御策略分类法，以减轻跨越整个杀伤链的 GenAI 引发的威胁。
• 为理解新兴的 GenAI 相关网络威胁与防御提供基础。

提出的方法

• 以 Cyber Kill Chain 作为基础模型，将 GenAI 基地攻击风险映射到 CKC 的每个阶段。
• 调研面向 GenAI 的攻击性技术（规避、适应/自动化、变形、欺骗、错误信息）及其在 CKC 的映射。
• 制定一套防御策略的分类法（检测、欺骗、对抗性训练等），以应对 GenAI 启用的威胁。
• 综合现有文献和威胁行为者的行为，勾勒出具备防御感知的策略。
• 突出挑战并利用新兴技术提出改进建议。

Figure 1 : Adapted from the work of Goodfellow et al. [ 15 ] , a taxonomy of GenAI based on the tractability of their density distributions. The left branch represents GenAI, which utilizes explicit density estimation techniques as its foundation, whereas the right branch represents GenAI, which lev

实验结果

研究问题

• RQ1 GenAI 基于的攻击如何映射到 Cyber Kill Chain 的每个阶段？
• RQ2 威胁行为者在 CKC 各阶段使用哪些面向 GenAI 的攻击技术？
• RQ3 哪些防御策略能够以攻击感知和自适应的方式减轻 GenAI 引发的网络威胁？
• RQ4 在以 GenAI 为焦点的网络防御中，关键挑战与改进方向是什么？

主要发现

• GenAI 启用的攻击可影响 CKC 的每个阶段，因此需要全面、分阶段的防御。
• 威胁行为者采用规避、适应/自动化、变形、欺骗和错误信息等策略来提升攻击效果。
• 提出一套防御策略分类法，用于在 CKC 各阶段检测、威慑和缓解 GenAI 引发的威胁。
• 本工作为理解新兴的 GenAI 驱动威胁并指导针对对抗性 GenAI 使用的防御设计奠定基础。

Figure 2 : Adapted from Lockheed Martin [ 10 ] , Cyber Kill Chain (CKC) stages illustrate the sequential progression of a cyberattack from reconnaissance to action on objectives. Each stage represents a crucial step in the threat actors’ strategies and methodologies, providing insights for defensive