Abstract
It is self-evident that recently, humanity has entered the fourth industrial
revolution. With the advent of the Internet of Things, cloud computing, and Artificial
Intelligence, industrial critical infrastructures such as power plants, oil and gas plants,
heavy industries, nuclear plants, and water treatment facilities are experiencing
disruptive growth. This era of industrialization, nevertheless, has carried with it its new
myriad of issues, notably regarding cyber security threats. Nowadays, industrial
processes are openly connected to the internet, and internet-connected machines are
almost always highly susceptible to security breaches by adversaries despite sufficient
cyber security safeguards. Intrusion detection systems (IDS) are designed to employ
classification models to detect malicious attacks such as service attacks, probing
attacks, etc. In intrusion detection, the phase that reduces the number of similar traffic
attributes while sustaining the accuracy of classification is a requirement that
considerably improves an intrusion detection system's overall efficacy. This chapter
focuses on (i) various feature selection methods in IDS, (ii) ML&DL classification
models in IDS of industrial systems, (iii) Various ensemble feature selection models
are analyzed, and a novel ensemble feature selection model for IDS is proposed.