The ai-based cyber threat landscape: A survey
Abstract
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.
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Literature [41, 42, 92] has collected and studied representative AI misuse cases, and summarized high-stake patterns that AI failures tend to follow [92], such as vulnerability to attacks, underperformance on noises, and biased prediction.
“Literature such as [41, 42, 92] has collected and studied representative AI misuse cases, and summarized high-stake patterns that AI failures tend to follow [92], such as vulnerability to attacks, underperformance on noises, and biased prediction.”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [41]
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- Authors
- Kaloudi, N., & Li, J.
- Affiliations
- Norwegian University of Science and Technology (NO)
- Venue
- ACM Computing Surveys
- Published
- 2020-02-06
- Volume
- 53
- Issue
- 1
- Pages
- 1-34
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3004908285
- Cited by
- 310
- References
- 44
- Citations by year
- 2026: 42 · 2025: 73 · 2024: 72 · 2023: 44 · 2022: 34 · 2021: 32 · 2020: 13
- FWCI
- 12.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Computing Surveys (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Keywords
- adversarial learning, attack detection, smart grid security, critical infrastructure security
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