TY - JOUR
AU - Nektaria Kaloudi
AU - Jingyue Li
AB -  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.
BT - Association for Computing Machinery (ACM)
DA - 2020-02-06
DO - 10.1145/3372823
N1 - 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.. 
N2 -  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.
PY - 2020
T2 - Association for Computing Machinery (ACM)
TI - The AI-Based Cyber Threat Landscape: A Survey
UR - https://dl.acm.org/doi/abs/10.1145/3372823
SN - 0360-0300
ER -