← authors · overview

• Evaluation Of Adversarial Training On Different Types Of Neural Networks In Deep Learning-based Idss
  2020 · 46 citations
• Evasion Adversarial Attacks Remain Impractical Against ML-based Network Intrusion Detection Systems, Especially Dynamic Ones
  2023 · 3 citations
• Threat Modelling using Domain-Adapted Language Models: Empirical Evaluation and Insights
  2026
• Exploring the Effect of DNN Depth on Adversarial Attacks in Network Intrusion Detection Systems
  2025
• LLMs' Suitability for Network Security: A Case Study of STRIDE Threat Modeling
  2025
• Introducing Adaptive Continuous Adversarial Training (ACAT) To Enhance ML Robustness
  2024
• Investigating Resistance Of Deep Learning-based IDS Against Adversaries Using Min-max Optimization
  2019
• A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS
  2024