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An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems

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 a...

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Main Authors: Qassim, Q., Ahmad, A.R., Ismail, R., Abu Bakar, A., Abdul Rahim, F., Mokhtar, M.Z., Ramli, R., Mohd Yusof, B., Mahdi, M.N.
Format: Conference Paper
Language:English
Published: 2020
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spelling my.uniten.dspace-130302020-07-06T07:06:32Z An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems Qassim, Q. Ahmad, A.R. Ismail, R. Abu Bakar, A. Abdul Rahim, F. Mokhtar, M.Z. Ramli, R. Mohd Yusof, B. Mahdi, M.N. 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. © 2019 IEEE. 2020-02-03T03:29:54Z 2020-02-03T03:29:54Z 2019 Conference Paper 10.1109/BigDataSecurity-HPSC-IDS.2019.00057 en
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description 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. © 2019 IEEE.
format Conference Paper
author Qassim, Q.
Ahmad, A.R.
Ismail, R.
Abu Bakar, A.
Abdul Rahim, F.
Mokhtar, M.Z.
Ramli, R.
Mohd Yusof, B.
Mahdi, M.N.
spellingShingle Qassim, Q.
Ahmad, A.R.
Ismail, R.
Abu Bakar, A.
Abdul Rahim, F.
Mokhtar, M.Z.
Ramli, R.
Mohd Yusof, B.
Mahdi, M.N.
An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems
author_facet Qassim, Q.
Ahmad, A.R.
Ismail, R.
Abu Bakar, A.
Abdul Rahim, F.
Mokhtar, M.Z.
Ramli, R.
Mohd Yusof, B.
Mahdi, M.N.
author_sort Qassim, Q.
title An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems
title_short An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems
title_full An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems
title_fullStr An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems
title_full_unstemmed An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems
title_sort anomaly detection technique for deception attacks in industrial control systems
publishDate 2020
_version_ 1672614200163172352
score 13.211869

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