The AI-Based Cyber Threat Landscape
By Nektaria Kaloudi, Jingyue Li. 2020
276 citations recorded by the source corpus.
Recent advancements in artificial intelligence (AI) technologies have induced tremendous growth in innovation and automation. Although these AI technologies offer significant benefits, they can be used maliciously. Highly targeted and evasive attacks in benign carrier applications, such as DeepLocker, have demonstrated the intentional use of AI for harmful purposes. Threat actors are constantly changing and improving their attack strategy with particular emphasis on the application of AI-driven techniques in the attack process, called AI-based cyber attack, which can be used in conjunction with conventional attack techniques to cause greater damage. Despite several studies on AI and security, researchers have not summarized AI-based cyber attacks enough to be able to understand the adversary’s actions and to develop proper defenses against such attacks. This study aims to explore existing studies of AI-based cyber attacks and to map them onto a proposed framework, providing insight into new threats. Our framework includes the classification of several aspects of malicious uses of AI during the cyber attack life cycle and provides a basis for their detection to predict future threats. We also explain how to apply this framework to analyze AI-based cyber attacks in a hypothetical scenario of a critical smart grid infrastructure.
Subfields: Computer Networks and Communications, Control and Systems Engineering, Privacy & Security, Signal Processing
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Citations in the corpus
- The Gradient of Generative AI Release: Methods and Considerations (2023, background): “Risks and threats from increasingly powerful systems are difficult to enumerate and assess, especially since malicious actors and their incentives are constantly evolving [44].”
- Unveiling Security, Privacy, and Ethical Concerns of ChatGPT (2023, background): “A similar attack in conventional attack is the evasion attack [21], which is the most common type of attack directed at machine learning models during inference.”
- Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges (2021, methodology): “Figure 23 summarizes most prospective defense methods from the studies in [205], [219], [227].”