A three-tiered intrusion detection system for industrial control systems. Issue 1 (27th February 2021)

• Record Type: Journal Article
• Authors: Anthi, Eirini; Williams, Lowri; Burnap, Pete; Jones, Kevin
• Is Part Of: Journal of cybersecurity. Volume 7:Issue 1(2021)
• Publication Date: 2021-02-27
• Subjects: supervised machine learning -- industrial control systems -- attack detection -- intrusion detection system -- networks; Computer security -- Periodicals; Computer networks -- Security measures -- Periodicals; 005.805
• DOI: 10.1093/cybsec/tyab006
• Languages: English
• ISSNs: 2057-2093

Abstract

Abstract: This article presents three-tiered intrusion detection systems, which uses a supervised approach to detect cyber-attacks in industrial control systems networks. The proposed approach does not only aim to identify malicious packets on the network but also attempts to identify the general and finer grain attack type occurring on the network. This is key in the industrial control systems environment as the ability to identify exact attack types will lead to an increased response rate to the incident and the defence of the infrastructure. More specifically, the proposed system consists of three stages that aim to classify: (i) whether packets are malicious; (ii) the general attack type of malicious packets (e.g. Denial of Service); and (iii) finer-grained cyber-attacks (e.g. bad cyclic redundancy check, attack). The effectiveness of the proposed intrusion detection systems is evaluated on network data collected from a real industrial gas pipeline system. In addition, an insight is provided as to which features are most relevant in detecting such malicious behaviour. The performance of the system results in an F -measure of: (i) 87.4%, (ii) 74.5% and (iii) 41.2%, for each of the layers, respectively. This demonstrates that the proposed architecture can successfully distinguish whether network activity is malicious and detect which general attack was deployed.