arXiv Machine Learning By Paul Whitten, Francis Wolff, Chris Papachristou

Explainability Methods for Hardware Trojan Detection: A Systematic Comparison

Read the original on arXiv Machine Learning →

arXiv:2601. 18696v5 Announce Type: replace Abstract: Hardware trojans are malicious circuits which compromise the functionality and security of an integrated circuit (IC).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Aug 4

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection

arXiv:2608. 01454v1 Announce Type: cross Abstract: Provenance-based intrusion detection systems (PIDS) frequently report strong performance, but the conclusions drawn from these results can be highly sensitive to benchmarking choices and evaluation protocols.

By Lorenzo Guerra, Thomas Chapuis, Guillaume Duc, Pavlo Mozharovskyi, Van-Tam Nguyen
arXiv AI
Jun 11

Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerability Benchmarks

arXiv:2605. 23243v2 Announce Type: replace-cross Abstract: We evaluate whether frontier LLMs are ready for cybersecurity through a dual-mode benchmark: white-box function-level vulnerability detection (VulnLLM-R, across C/Java/Python) and black-box web application security testing (five production-style applications with 118 ground-truth vulnerabilities across 20+ CWE families, which we will open-source).

By Vivek Dahiya, Sunny Nehra, Vipul Dholariya, Bhavik Shangari, Chandra Khatri