Author(s):
Journal
Association for Computing Machinery (ACM)
Abstract
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.
Concluding remarks
Threat actors are constantly changing and improving their attack performance with a particular emphasis on the application of AI-driven techniques in the attack process. This study investigates the offensive capabilities through automation of traditionally manual processes, allowing attackers to conduct attacks of a wider scope, at a faster speed, and on a larger scale. In this article, we explored research examples of cyber attacks, posed by combining the “dark” side of AI with the attack techniques. We introduced an analytic framework for modeling those attacks that can be useful in understanding their context and identified key opportunity areas for the security community in implementing suitable defenses. Finally, we illustrated a scenario to show that an sCPS (e.g., SG) can be the target of more advanced malicious cyber activity..
Reference details
DOI
10.1145/3372823
Resource type
Journal Article
Year of Publication
2020
ISSN Number
0360-0300
Publication Area
Cybersecurity and defense
Date Published
2020-02-06
How to cite this reference:
Kaloudi, N., & Li, J. (2020). The AI-Based Cyber Threat Landscape: A Survey. Association for Computing Machinery (ACM). https://doi.org/10.1145/3372823 (Original work published)