Robustness of Machine Learning-Based Intrusion Detection Systems Against Adversarial Attacks in Cyber-Physical Systems - Silva Júnior, Antonio Carlos C. da; Quincozes, Silvio E.; Cattelan, Renan G.; Miani, Rodrigo S. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2024.245249, 2024)
Cyber-physical systems are integral to various technological ecosystems, enabling interactions between computing systems, communication networks, and physical processes. Machine learning algorithms are employed to train anomaly-based Intrusion Detection Systems (IDS) to secure these ecosystems. However, they remain vulnerable to adversarial attacks, where training and testing data can be manipulated to degrade performance. This paper evaluates machine learning algorithms in anomaly-based IDSs under adversarial attacks using two datasets from the power system context. The results indicate that ensemble algorithms excelled against JSMA attacks, while single classifiers performed better against FGSM attacks, with Decision Tree and Random Forest being the top performers in their respective groups.