Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems
A new research paper investigates the robustness of machine learning models used in network intrusion detection systems against adversarial attacks. The study found that while Random Forest models achieved high baseline accuracy, they catastrophically failed under adversarial pressure. In contrast, Convolutional Neural Networks (CNNs) demonstrated greater resilience, maintaining high accuracy even with increasing perturbation levels, suggesting CNNs are a more suitable choice for adversarial environments. AI
IMPACT CNNs offer greater resilience against adversarial attacks in network intrusion detection, guiding practitioners toward more secure deployments.