An Investigation of Performance Analysis of Anomaly Detection Techniques for Big Data in SCADA Systems

An Investigation of Performance Analysis of Anomaly Detection Techniques for Big Data in SCADA Systems

Journal

Subject and more

  • LCC Subject Category: Computer and Information Science, Industrial Management, Data Communication and Networks
  • Publisher's keywords: Anomaly detection, SCADA systems, big data
  • Language of fulltext: english
  • Full-text formats available: PDF

AUTHORS

    Mohiuddin Ahmed, Adnan Anwar, Abdun Naser Mahmood, Zubair Shah, Michael J. Maher

EDITORIAL INFORMATION

FULL TEXT

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ABSTRACT

Anomaly detection is an important aspect of data mining, where the main objective is to identify anomalous or unusual data from a given dataset. However, there is no formal categorization of application-specific anomaly detection techniques for big data and this ignites a confusion for the data miners. In this paper, we categorise anomaly detection techniques based on nearest neighbours, clustering and statistical approaches and investigate the performance analysis of these techniques in critical infrastructure applications such as SCADA systems. Extensive experimental analysis is conducted to compare representative algorithms from each of the categories using seven benchmark datasets (both real and simulated) in SCADA systems. The effectiveness of the representative algorithms is measured through a number of metrics. We highlighted the set of algorithms that are the best performing for SCADA systems.

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