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Q-MALDetect: Quantum Image-Based Malware Detection and Adversarial Robustness Analysis

Walid El Maouaki, Mohammad Izen, Nouhaila Innan, Alberto Marchisio, Muhammad Shafique · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26240 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

该论文介绍了Q-MALDetect,一个较强的混合式和变量量量子检测器,用于安全应用,但它们在恶意软件分析中的对立强度仍然不够理解。该论文介绍了Q-MALDetect,一个用于x86-64 Executable 和 Linkable Format (ELF) 二进制的字节图像表示的混合式和变量量量子检测器,并且在功能空间和一系列Grad-CAM引导的,可执行意识的逃避攻击中对其进行评估。该模型实现了97.2%的训练准确性,但其强度随着攻击强度的增加而恶化,最强的功能空间攻击达到78.7%的攻击成功率(RAS)。在这些可执行意识攻击中,Grad-

Machine translation (Meta M2M100); the English original follows.

Abstract

Quantum machine-learning (QML) models are increasingly considered for security applications, yet their adversarial robustness in malware analysis remains insufficiently understood. This paper presents Q-MALDetect, a hybrid convolutional and variational-quantum detector for byte-image representations of x86-64 Executable and Linkable Format (ELF) binaries, and evaluates it under feature-space and a range of Grad-CAM-guided, executable-aware evasion attacks. The model achieves 97.2% training accuracy, but its robustness deteriorates as attack strength increases, with the strongest evaluated feature-space attack reaching a 78.7% attack success rate (ASR). Among executable-aware attacks, Grad-CAM-guided no-operation (NOP) insertion produces the highest conditional ASR of 32.9%. Dynamic adversarial training is the most effective evaluated defense, reducing feature-space ASR to as low as 5.0%, executable-aware ASR to 7.7%, and the sequential-attack ASR to 34.0%. In contrast, convolutional-autoencoder preprocessing increases executable-aware ASR to 39.7%, coinciding with a rise in Grad-CAM attribution mass within the mutable .text region from 35.2% to 45.7%. Ablation results reveal no monotonic relationship between qubit count and robustness, while the encoding interface modifies the learned attack surface without neutralizing the strongest attack. These findings demonstrate that the effectiveness of attacks and defenses depends strongly on the perturbation constraints and evaluation conditions. Reliable deployment of QML malware detectors therefore requires assessment against both feature-space and executable-aware attacks rather than reliance on clean accuracy or generic denoising alone.

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Topics: Computer science, Robustness (evolution), Malware, Adversary, Computer security, Preprocessor

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange