The relevance, effectiveness, and future prospects of cyber deception implementation within organizations

Kaila Eng; John King; Carmen Schillaci; Atul Rawal

doi:10.1117/12.3013114

7 June 2024 The relevance, effectiveness, and future prospects of cyber deception implementation within organizations

Kaila Eng, John King, Carmen Schillaci, Atul Rawal

Proceedings Volume 13054, Assurance and Security for AI-enabled Systems; 130540M (2024) https://doi.org/10.1117/12.3013114
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) based systems have seen tremendous progress in the past years. This unprecedent growth has also opened new challenges and vulnerabilities for keeping AI/ML based systems safe and secure. With a multitude of studies investigating adversarial machine learning (AML) and cyber security for AI/ML systems, there is a need for novel techniques and methodologies for securing these systems. Cyber security is often used as a blanket term meaning all defenses used in the context of cyber. This leaves out methodologies and techniques, being used more offensively, such as cyber deception. This study provides a comprehensive overview of cyber-deception for securing AI/ML systems including its relevance, effectiveness, and its potential for AI/ML assurance. The study provides an overview of behavioral sciences for cyber-deception, the benefits of using cyber deception, and the ethical concerns associated with cyber deception. Additionally, we present a use-case for the utilization of cyber deception with zero-trust architecture (ZTA) for assurance and security for AI/ML based systems.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kaila Eng, John King, Carmen Schillaci, and Atul Rawal "The relevance, effectiveness, and future prospects of cyber deception implementation within organizations", Proc. SPIE 13054, Assurance and Security for AI-enabled Systems, 130540M (7 June 2024); https://doi.org/10.1117/12.3013114

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