survey

The AI-Based Cyber Threat Landscape: A Survey

Authors:

Nektaria Kaloudi,

Jingyue LiAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 1

Article No.: 20, Pages 1 - 34

https://doi.org/10.1145/3372823

Published: 06 February 2020 Publication History

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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Index Terms

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1. General and reference
  1. Document types
    1. Surveys and overviews

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 53, Issue 1

January 2021

781 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3382040

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 06 February 2020

Accepted: 01 November 2019

Revised: 01 October 2019

Received: 01 May 2019

Published in CSUR Volume 53, Issue 1

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