Author

Chris Hicks

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#artificial intelligence Review Dec 2024

Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points

Security vulnerabilities in software can have severe consequences; however, manual vulnerability detection is costly and does not scale, especially as agentic coding frameworks increase the rate of code production. Over the last decade, a large body of research has applied machine learning machine learning to automate...

Dan Ristea, Shae McFadden, Ezzeldin Shereen et al. · 1 citation

Preprint Aug 2026

Proving the Utility of Large Language Models in Cybersecurity Simulations: A Comprehensive Examination

YAML is employed as a structured representation format for simulating complex network configurations, thereby enabling Large Language Model-driven pipelines to support and improve reinforcement learning (RL) agent training, and underscore the transformative potential of integrating LLMs into cybersecurity research.

S. Kampakis, F. Rovai, Marcos Charalambides et al. · 1 citation

#machine learning Preprint Aug 2026

REPLICANT: Learning Policies for Evading and Hardening Malware Detectors

This work presents Replicant, a deep reinforcement learning framework that learns the realistic task of evasion under a strict label-only black-box threat model and demonstrates that learning the task of evasion not only results in stronger attack performance but provides a better signal for hardening malware detectors...

Shae McFadden, Ilias Tsingenopoulos, Mario D'Onghia et al. · 0 citations