Estimating adversarial transferability from feature-importance similarity in explainable intrusion detection systems

Machine Learning (ML)-based Intrusion Detection Systems (IDS) are critical for defending against cyber threats but remain vulnerable to adversarial attacks, posing risks to system reliability and security. This study investigates the transferability of adversarial attacks across XGBoost and LightGBM