ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR INTRUSION DETECTION SYSTEMS

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR INTRUSION DETECTION SYSTEMS

Prof. Dr. Sandip D Satav,

Associate Professor, JSPM’s Jayawantrao Sawant College of Engineering,

Department of Information Technology, Hadapsar, Pune.

ABSTRACT

Intrusion detection systems (IDS) play a critical role in safeguarding computer networks against malicious activities and unauthorized access. With the ever-growing sophistication of cyber threats, traditional rule-based and signature-based IDS approaches have shown limitations in detecting emerging and unknown attacks. As a result, there has been a surge of interest in leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance the detection capabilities of IDS. This paper provides a comprehensive review of recent advancements in AI and ML for intrusion detection systems, covering various techniques, methodologies, datasets, and evaluation metrics. Furthermore, it discusses the challenges, limitations, and future directions in this field.

Keywords: IDS, AI, ML, SVM, CNN, DNN, RNN, etc.