GMA-SAWGAN-GP: A Novel Data Generative Framework to Enhance IDS Detection Performance

Intrusion Detection System IDS is often calibrated to known attacks and generalizes poorly to unknown threats. This paper proposes GMA-SAWGAN-GP, a novel generative augmentation framework built on a Self-Attention-enhanced Wasserstein GAN with Gradient Penalty WGAN-GP. The generator employs...

Teaching an Old LLM Secure Coding: Localized Preference Optimization on Distilled Preferences

LLM generated code often contains security issues. We address two key challenges in improving secure code generation. First, obtaining high quality training data covering a broad set of security issues is critical. To address this, we introduce a method for distilling a preference dataset of...