Adversarial Attacks and Defense Mechanisms in the Age of Quantum Computing

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Author(s): Yara Shamoo (Saint Leo University, USA)
Copyright: 2025
Pages: 44
Source title: Leveraging Large Language Models for Quantum-Aware Cybersecurity
Source Author(s)/Editor(s): Hewa Majeed Zangana (Duhok Polytechnic University, Iraq)and Marwan Omar (Illinois Institute of Technology, USA)
DOI: 10.4018/979-8-3373-1102-9.ch010

Keywords: Cyber and Network Security / Information Science Reference / Knowledge Management and Library Science / Natural Language Processing

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Abstract

Adversarial attacks on machine learning models have become a significant concern in cybersecurity, especially with the advent of quantum computing. These attacks aim to manipulate the decision-making process of AI systems, leading to vulnerabilities that can be exploited by malicious actors. As quantum computing promises to revolutionize various industries, it also introduces new challenges for defending against adversarial threats. This chapter explores the impact of quantum computing on adversarial machine learning, examining how quantum algorithms can be both a tool for enhancing attack strategies and a foundation for developing more robust defense mechanisms. It reviews existing defense techniques, such as adversarial training and gradient masking, and discusses the potential for quantum-aware models to counteract these threats.

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