A Three-Axis Stress Test of LLM Vs Classical ML for Network Intrusion Detection under Distribution Shift and Adversarial Evasion

Large language models are increasingly benchmarked against classical machine learning for network intrusion detection NIDS, almost always using same-dataset evaluation, and that protocol turns out to be incomplete. Evaluating XGBoost and RoBERTa-LoRA on two independently collected NetFlow v2...

REPLICANT: Learning Policies for Evading and Hardening Malware Detectors

To determine the real-world effectiveness of machine learning based malware detection, it is vital to evaluate its robustness against highly capable adversaries. However, state-of-the-art attacks do not effectively model realistic adversaries, as they often assume access to privileged information...

Explainable Adaptive Zero Trust Framework for AWS with Adversarial Robustness Evaluation

Cloud environments built on Amazon Web Services face a structural security vulnerability: once a credential passes authentication, the resulting session is often treated as trusted for its entire duration. This assumption fails when credentials are stolen. We introduce the Explainable Adaptive Ze...

Detecting Adversarial Evasion Attacks against Autoencoder-Based Network Intrusion Detection Systems

Evasion attacks deliberately manipulate input to an ML-based system to produce an incorrect prediction while the manipulated input still appears benign. The PANDA framework has demonstrated that adversarial examples developed for the vision domain can be transferred to the network domain by...