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

Fingerprint

Control systems
Intrusion detection
Internet
Network protocols
Deception
Attack
Anomaly detection
Precaution
Communication
Interaction
Threat
World Wide Web

All Science Journal Classification (ASJC) codes

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

Cite this

Qassim, Q., Ahmad, A. R., Ismail, R., Abu Bakar, A., Abdul Rahim, F., Mokhtar, M. Z., ... 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
Qassim, Qais ; Ahmad, Abdul Rahim ; Ismail, Roslan ; Abu Bakar, Asmidar ; Abdul Rahim, Fiza ; Mokhtar, Mohd Zin ; Ramli, Ramona ; Mohd Yusof, Busyra ; Mahdi, Mohammed Najah. / An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems. 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., 2019. pp. 267-272 (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).
@inproceedings{509fbbace5264c3ba07b65a4c14af699,
title = "An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems",
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.",
author = "Qais Qassim and Ahmad, {Abdul Rahim} and Roslan Ismail and {Abu Bakar}, Asmidar and {Abdul Rahim}, Fiza and Mokhtar, {Mohd Zin} and Ramona Ramli and {Mohd Yusof}, Busyra and Mahdi, {Mohammed Najah}",
year = "2019",
month = "5",
day = "1",
doi = "10.1109/BigDataSecurity-HPSC-IDS.2019.00057",
language = "English",
series = "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",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "267--272",
booktitle = "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",
address = "United States",

}

Qassim, Q, Ahmad, AR, Ismail, R, Abu Bakar, A, Abdul Rahim, F, Mokhtar, MZ, Ramli, R, Mohd Yusof, B & Mahdi, MN 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., 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., pp. 267-272, 5th 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, 27/05/19. https://doi.org/10.1109/BigDataSecurity-HPSC-IDS.2019.00057

An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems. / Qassim, Qais; Ahmad, Abdul Rahim; Ismail, Roslan; Abu Bakar, Asmidar; Abdul Rahim, Fiza; Mokhtar, Mohd Zin; Ramli, Ramona; Mohd Yusof, Busyra; Mahdi, Mohammed Najah.

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., 2019. p. 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).

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

TY - GEN

T1 - An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems

AU - Qassim, Qais

AU - Ahmad, Abdul Rahim

AU - Ismail, Roslan

AU - Abu Bakar, Asmidar

AU - Abdul Rahim, Fiza

AU - Mokhtar, Mohd Zin

AU - Ramli, Ramona

AU - Mohd Yusof, Busyra

AU - Mahdi, Mohammed Najah

PY - 2019/5/1

Y1 - 2019/5/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85072773110&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85072773110&partnerID=8YFLogxK

U2 - 10.1109/BigDataSecurity-HPSC-IDS.2019.00057

DO - 10.1109/BigDataSecurity-HPSC-IDS.2019.00057

M3 - Conference contribution

AN - SCOPUS:85072773110

T3 - 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

SP - 267

EP - 272

BT - 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

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Qassim Q, Ahmad AR, Ismail R, Abu Bakar A, Abdul Rahim F, Mokhtar MZ et al. 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. Institute of Electrical and Electronics Engineers Inc. 2019. p. 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). https://doi.org/10.1109/BigDataSecurity-HPSC-IDS.2019.00057