Novel Intrusion Detection Mechanism with Low Overhead for SCADA Systems

Leandros Maglaras, Helge Janicke, Jianmin Jiang, Andrew Crampton

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Citations (Scopus)


SCADA (Supervisory Control and Data Acquisition) systems are a critical part of modern national critical infrastructure (CI) systems. Due to the rapid increase of sophisticated cyber threats with exponentially destructive effects, intrusion detection systems (IDS) must systematically evolve. Specific intrusion detection systems that reassure both high accuracy, low rate of false alarms and decreased overhead on the network traffic must be designed for SCADA systems. In this book chapter we present a novel IDS, namely K-OCSVM, that combines both the capability of detecting novel attacks with high accuracy, due to its core One-Class Support Vector Machine (OCSVM) classification mechanism and the ability to effectively distinguish real alarms from possible attacks under different circumstances, due to its internal recursive k-means clustering algorithm. The effectiveness of the proposed method is evaluated through extensive simulations that are conducted using realistic datasets extracted from small and medium sized HTB SCADA testbeds.
Original languageEnglish
Title of host publicationSecuring the Internet of Things
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
EditorsInformation Resources Management Association, Mehdi Khosrow-Pour
PublisherIGI Global
Number of pages20
ISBN (Electronic)9781522598671
ISBN (Print)9781522598664, 1522598669
Publication statusPublished - 6 Sep 2019


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