Empirical comparison of machine learning algorithms for mitigating power systems intrusion attacks

Oyeniyi Akeem Alimi, Khmaies Ouahada, Adnan M. Abu-Mahfouz, Kuburat Oyeranti Adefemi Alimi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

The normal and stable operation of the modern power systems rely on accurate situational awareness and visibility as recent researches and experiences have shown that the cyber-physical infrastructures are highly vulnerable to cyberattacks and intrusions. Attackers can design various intrusive injections to disrupt the operation thereby triggering failures, loss of synchronism, economic losses and sometimes injuries to employees. Hence, there have continuously been crucial need for timely, accurate identification and detection of these intrusions. Several traditional intrusion detection systems proposed in the literature have proven inefficient as they are computationally incompetent for the complex nature of the modern power systems. An alternative has been identified in form of machine learning techniques. This paper presents an empirical comparison of five prominent machine learning algorithms: K-nearest neighbors, Decision Tree, Naive Bayes, Random Forest and AdaBoost for predicting intrusion attacks into power systems network. The idea is to present the best possible classifier for the analyzed test systems and also to show that each of the developed algorithms can perform exceptionally well within some context. The developed algorithms were evaluated using a simulated voltage dataset generated from a load flow analysis of a 24-bus power systems case study. Experimental analysis and results obtained showed the feasibility of applying machine learning techniques in successfully predicting and detecting intrusions into power systems network.

Original languageEnglish
Title of host publication2020 International Symposium on Networks, Computers and Communications, ISNCC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728156286
DOIs
Publication statusPublished - 20 Oct 2020
Event2020 International Symposium on Networks, Computers and Communications, ISNCC 2020 - Montreal, Canada
Duration: 20 Oct 202022 Oct 2020

Publication series

Name2020 International Symposium on Networks, Computers and Communications, ISNCC 2020

Conference

Conference2020 International Symposium on Networks, Computers and Communications, ISNCC 2020
Country/TerritoryCanada
CityMontreal
Period20/10/2022/10/20

Keywords

  • AdaBoost
  • Decision Tree
  • Intruder Detection
  • K-nearest Neighbors
  • Machine learning
  • Naive Bayes
  • Power systems
  • Random Forest

ASJC Scopus subject areas

  • Instrumentation
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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