Výsledky vyhledávání - "Hu, James Lee"

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Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach

Autor: Etter, Brian, Hu, James Lee, Ebrahimi, Mohammedreza, Li, Weifeng, Li, Xin, Chen, Hsinchun

Adversarial Malware Generation (AMG), the generation of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors has emerged as a crucial tool in the development of proactive cyberdefense. However, the majority of extant

Externí odkaz: http://arxiv.org/abs/2402.02600

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Multi-view Representation Learning from Malware to Defend Against Adversarial Variants

Autor: Hu, James Lee, Ebrahimi, Mohammadreza, Li, Weifeng, Li, Xin, Chen, Hsinchun

Deep learning-based adversarial malware detectors have yielded promising results in detecting never-before-seen malware executables without relying on expensive dynamic behavior analysis and sandbox. Despite their abilities, these detectors have been

Externí odkaz: http://arxiv.org/abs/2210.15429

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Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach

Autor: Hu, James Lee, Ebrahimi, Mohammadreza, Chen, Hsinchun

Deep Learning (DL)-based malware detectors are increasingly adopted for early detection of malicious behavior in cybersecurity. However, their sensitivity to adversarial malware variants has raised immense security concerns. Generating such adversari

Externí odkaz: http://arxiv.org/abs/2112.01724

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