Jul 2026· GECCO Companion· pp. 537-540· 0 citations· 27 references
Computer Science
TL;DR
A co-evolutionary adversarial training framework that integrates gradient-based adversarial attacks with evolutionary controllers to improve robustness is proposed, exemplifying a co-evolutionary arms-race framework for realistic, feature-constrained malware detection.
Abstract
Adversarial robustness remains a significant challenge in machine-learning malware detection. We propose a co-evolutionary adversarial training framework that integrates gradient-based adversarial attacks with evolutionary controllers to improve robustness. A hybrid Convolutional Neural Network (CNN-MLP) processes spatial and vector-based features, while evolved controllers adaptively guide Projected Gradient Descent (PGD) using gradient statistics, optimizing adversarial effectiveness while respecting feature constraints. This online, co-evolutionary process exposes the classifier to increasingly adaptive attacks. Experimental results demonstrate that the proposed approach enhances adversarial robustness relative to standard training while maintaining clean accuracy, exemplifying a co-evolutionary arms-race framework for realistic, feature-constrained malware detection.
This paper introduces DefendMal, a novel framework that synergistically combines Denoise Autoencoder with Sequence Squeezing, a Context-aware Adversarial Generator (CAG-AdvGAN), Projected Gradient Descent (PGD) adversarial training, and a Positive–Negative Detector with Variational Autoencoder (PNDetector-VAE) to enhan...
Dennis Benedict Crasta, Vikash Kumar· Journal of Computer Virology...· 0 citations
A systematic framework to enhance adversarial robustness is proposed, validated on the Malimg dataset and supersedes previous approaches by 13.15% in terms of the evasion rate and 37.34% in terms of retraining success.
Muhammad Arham Tariq, Allah Bux Sargano, Z. Habib et al.· International Journal of Inf...· 0 citations
A comprehensive survey of how GAN-based methods are utilized for identifying unusual and harmful activities in cyber settings and addresses ongoing challenges and potential future avenues for employing GANs to counteract emerging cybersecurity threats.
A. Thakore, Neha Gupta, Akash Saxena et al.· International Journal of Inn...· 0 citations
The results demonstrate that GNN-based malware detection not only addresses the limitations of conventional approaches in terms of scalability but also provides a more robust and adaptable framework that could be integrated into future real-time threat intelligence and automated defense systems.
Wurood A. Jbara, N. A. Hussein· Al-Noor Journal of Engineeri...· 0 citations
This study evaluates the robustness of multimodal learning-based IDS against transferable adversarial examples (AEs) generated by Generative Adversarial Networks (GANs) and integrates adversarial training techniques to enhance the IDS’s capability to identify attack patterns with small perturbations.
Duy The Phan, Cao The Thuan, Q. Doan et al.· Journal on spesial topics in...· 0 citations
This paper evaluates the robustness of the Support Vector Machine (SVM) classifier, a leading algorithm in state-of-the-art HT detection frameworks, under gradient-based adversarial attacks, and highlights the need to reframe hardware security evaluations beyond nominal accuracy toward adversarial robustness.
Ashutosh Ghimire, Lingwei Chen, Cole Castronova et al.· Journal of electronic testin...· 0 citations
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