An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems

Qais Qassim, Abdul Rahim Ahmad, Roslan Ismail, Asmidar Abu Bakar, Fiza Abdul Rahim, Mohd Zin Mokhtar, Ramona Ramli, Busyra Mohd Yusof, Mohammed Najah Mahdi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The increasing interaction of modern industrial control systems (ICS) to the outside Internet world influences making these systems vulnerable to a wide range of cyber-attacks. Moreover, the utilisation of Commercial-off-the-Shelf (COTS) products, as well as open communication protocols, made them attractive targets to various threat agents including cyber-criminals, national-state, and cyber-terrorists. Given that, today's ICSs are deriving the most critical national infrastructures. Therefore, this raises tremendous needs to secure these systems against cyber-attacks. Intrusion detection technology has been considered as one of the most essential security precautions for ICS networks. It can effectively detect potential cyber-attacks and malicious activities and prevent catastrophic consequences. This paper puts forward a new method to detect malicious activities at the ICS net-works.

Original languageEnglish
Title of host publicationProceedings - 5th IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2019, 5th IEEE International Conference on High Performance and Smart Computing, HPSC 2019 and 4th IEEE International Conference on Intelligent Data and Security, IDS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages267-272
Number of pages6
ISBN (Electronic)9781728100067
DOIs
Publication statusPublished - 01 May 2019
Event5th IEEE International Conference on Big Data Security on Cloud, 5th IEEE International Conference on High Performance and Smart Computing and 4th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2019 - Washington, United States
Duration: 27 May 201929 May 2019

Publication series

NameProceedings - 5th IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2019, 5th IEEE International Conference on High Performance and Smart Computing, HPSC 2019 and 4th IEEE International Conference on Intelligent Data and Security, IDS 2019

Conference

Conference5th IEEE International Conference on Big Data Security on Cloud, 5th IEEE International Conference on High Performance and Smart Computing and 4th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2019
CountryUnited States
CityWashington
Period27/05/1929/05/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Qassim, Q., Ahmad, A. R., Ismail, R., Abu Bakar, A., Abdul Rahim, F., Mokhtar, M. Z., Ramli, R., Mohd Yusof, B., & Mahdi, M. N. (2019). An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems. In Proceedings - 5th IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2019, 5th IEEE International Conference on High Performance and Smart Computing, HPSC 2019 and 4th IEEE International Conference on Intelligent Data and Security, IDS 2019 (pp. 267-272). [8819478] (Proceedings - 5th IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2019, 5th IEEE International Conference on High Performance and Smart Computing, HPSC 2019 and 4th IEEE International Conference on Intelligent Data and Security, IDS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigDataSecurity-HPSC-IDS.2019.00057