INTRUSION DETECTION USING MACHINE LEARNING FOR INDUSTRIAL CONTROL SYSTEMS

Sammanfattning: An intrusion detection system (IDS) is a software application that monitors a network forunauthorized and malicious activities or security policy violations related to confidentiality,integrity, and availability of a system. In this thesis, we performed detailed literature reviewson the different types of IDS, anomaly detection methods, and machine learning algorithmsthat can be used for detection and classification. We propose a hybrid intrusion detectionsoftware architecture for IDS using machine learning algorithms. By placing appropriatemachine learning algorithms in the existing detection systems, improvements in attack detectionand classification can be obtained. We have also attempted to compare the machine learningalgorithms by testing them in a simulated environment to make performance evaluations. Ourapproach provides indicators in selecting machine learning algorithms that can be used for ageneric intrusion detection system in the context of industrial control applications.