Hardware-Assisted Multi-Stage Ransomware Detection with Adaptive Machine Learning
Ransomware is a fast-escalating privacy threat causing significant financial and data losses worldwide. As attacks grow more sophisticated, traditional static signatures and dynamic behavior analysis struggle to defend. In this paper, we propose an novel framework that couples software-level scanning with hardware-level microprocessor activity monitoring to achieve efficient ransomware detection and mitigation. The contributions are threefold: (1) we employ adversarial training to harden static analysis against obfuscation and leverage hardware-assisted monitoring for real-time detection; (2) we integrate neural architecture search (NAS) to automatically tailor models for stronger general...
| Authors | Salma Aboelmagd, Md. Rakibul Ahasan, Zhixin Pan et al. |
|---|---|
| Primary category | cs.CR |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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