newsResearchers have developed Vul2Safe, a new framework for generating secure code using large language models, which addresses security vulnerabilities through self-reflection and token-level reinforcement learning. The approach introduces the PrimeVul+ dataset and SRCode training framework to provide more precise optimization of security patterns in code generation.
Researchers developed Hybrid Class-Aware Selective Replay (Hybrid-CASR), a continual learning method that improves AI-based software vulnerability detection by addressing catastrophic forgetting in temporal scenarios. The method achieved 0.667 Macro-F1 score while reducing training time by 17% compared to baseline approaches on CVE data from 2018-2024.
Researchers developed MI²DAS, a multi-layer intrusion detection framework for Industrial IoT networks that uses incremental learning to adapt to new cyber threats. The system achieved strong performance across multiple layers, with 95.3% accuracy in normal-attack discrimination and robust detection of both known and unknown attacks.
Researchers conducted a benchmark study on IoT botnet intrusion detection systems, finding that models trained on one network domain suffer significant performance degradation when applied to different environments. The study evaluated three feature sets across four IoT datasets and provided guidelines for improving cross-domain robustness through better feature engineering and algorithm selection.
Researchers introduce Jailbreak Foundry (JBF), a system that automatically converts AI jailbreak research papers into executable code modules for standardized testing. The system successfully reproduced 30 attacks with high accuracy and reduces implementation code by nearly half while enabling consistent evaluation across multiple AI models.