ResearchSpace

Intrusion detection for water distribution systems based on an hybrid particle swarm optimization with back propagation neural network

Show simple item record

dc.contributor.author Oyeniyi, AA
dc.contributor.author Ouahada, K
dc.contributor.author Abu-Mahfouz, Adnan MI
dc.contributor.author Rimer, S
dc.contributor.author Alimi, KO
dc.date.accessioned 2021-11-19T12:04:59Z
dc.date.available 2021-11-19T12:04:59Z
dc.date.issued 2021-09
dc.identifier.citation Oyeniyi, A., Ouahada, K., Abu-Mahfouz, A.M., Rimer, S. & Alimi, K. 2021. Intrusion detection for water distribution systems based on an hybrid particle swarm optimization with back propagation neural network. http://hdl.handle.net/10204/12161 . en_ZA
dc.identifier.isbn 978-1-6654-1984-0
dc.identifier.issn 2153-0025
dc.identifier.issn 2153-0033
dc.identifier.uri DOI: 10.1109/AFRICON51333.2021.9570951
dc.identifier.uri http://hdl.handle.net/10204/12161
dc.description.abstract The increasing integration of advanced information and communication tools in industrial control systems (ICS) has vastly increased the vulnerabilities and threats of intrusions into the various critical infrastructures which include the water distribution system, electrical power system, etc. that rely on the ICS systems. Currently, providing and ensuring adequate security for these ICS infrastructures are major concerns globally. The quick and accurate detection of any intrusive action into the ICS systems is highly important. Traditional intrusion detection systems (IDS) have exhibited worrying forms of limitations and shortcomings due to the heterogeneity of different cyberattacks and intrusions. Thus, there are needs to devise effective security measures. This paper proposes an IDS model based on the hybridization of particle swarm optimization (PSO) with back-propagation neural network (BPNN) for classifying intrusions in water system infrastructure. The PSO is used to optimize the parameters for the BPNN, thus improving the efficiency of classification. For the validation of the proposed method, the iTrust Lab's secure water treatment dataset was used for experimentation. Using prominent classification metrics, the 97% accuracy and 98.7% precision results achieved using the developed BPNN-PSO model is better compared to other methods including models from related works. Thus, the proposed model can meet the requirements of cyberattacks and intrusions detection in practical water distribution infrastructure. en_US
dc.format Abstract en_US
dc.language.iso en en_US
dc.relation.uri https://ieeexplore.ieee.org/document/9570951 en_US
dc.source IEEE Africon, Arusha, Tanzania, 3-15 September 2021 en_US
dc.subject Critical infrastructures en_US
dc.subject Industrial control systems en_US
dc.subject ICS en_US
dc.subject Back-propagation neural network en_US
dc.subject Particle swarm optimization en_US
dc.subject Secure water treatment en_US
dc.subject Water distribution systems en_US
dc.title Intrusion detection for water distribution systems based on an hybrid particle swarm optimization with back propagation neural network en_US
dc.type Conference Presentation en_US
dc.description.pages 5 en_US
dc.description.note ©2021 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website: https://ieeexplore.ieee.org/document/9570951 en_US
dc.description.cluster Next Generation Enterprises & Institutions en_US
dc.description.impactarea EDT4IR Management en_US
dc.identifier.apacitation Oyeniyi, A., Ouahada, K., Abu-Mahfouz, A. M., Rimer, S., & Alimi, K. (2021). Intrusion detection for water distribution systems based on an hybrid particle swarm optimization with back propagation neural network. http://hdl.handle.net/10204/12161 en_ZA
dc.identifier.chicagocitation Oyeniyi, AA, K Ouahada, Adnan MI Abu-Mahfouz, S Rimer, and KO Alimi. "Intrusion detection for water distribution systems based on an hybrid particle swarm optimization with back propagation neural network." <i>IEEE Africon, Arusha, Tanzania, 3-15 September 2021</i> (2021): http://hdl.handle.net/10204/12161 en_ZA
dc.identifier.vancouvercitation Oyeniyi A, Ouahada K, Abu-Mahfouz AM, Rimer S, Alimi K, Intrusion detection for water distribution systems based on an hybrid particle swarm optimization with back propagation neural network; 2021. http://hdl.handle.net/10204/12161 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Oyeniyi, AA AU - Ouahada, K AU - Abu-Mahfouz, Adnan MI AU - Rimer, S AU - Alimi, KO AB - The increasing integration of advanced information and communication tools in industrial control systems (ICS) has vastly increased the vulnerabilities and threats of intrusions into the various critical infrastructures which include the water distribution system, electrical power system, etc. that rely on the ICS systems. Currently, providing and ensuring adequate security for these ICS infrastructures are major concerns globally. The quick and accurate detection of any intrusive action into the ICS systems is highly important. Traditional intrusion detection systems (IDS) have exhibited worrying forms of limitations and shortcomings due to the heterogeneity of different cyberattacks and intrusions. Thus, there are needs to devise effective security measures. This paper proposes an IDS model based on the hybridization of particle swarm optimization (PSO) with back-propagation neural network (BPNN) for classifying intrusions in water system infrastructure. The PSO is used to optimize the parameters for the BPNN, thus improving the efficiency of classification. For the validation of the proposed method, the iTrust Lab's secure water treatment dataset was used for experimentation. Using prominent classification metrics, the 97% accuracy and 98.7% precision results achieved using the developed BPNN-PSO model is better compared to other methods including models from related works. Thus, the proposed model can meet the requirements of cyberattacks and intrusions detection in practical water distribution infrastructure. DA - 2021-09 DB - ResearchSpace DP - CSIR J1 - IEEE Africon, Arusha, Tanzania, 3-15 September 2021 KW - Critical infrastructures KW - Industrial control systems KW - ICS KW - Back-propagation neural network KW - Particle swarm optimization KW - Secure water treatment KW - Water distribution systems LK - https://researchspace.csir.co.za PY - 2021 SM - 978-1-6654-1984-0 SM - 2153-0025 SM - 2153-0033 T1 - Intrusion detection for water distribution systems based on an hybrid particle swarm optimization with back propagation neural network TI - Intrusion detection for water distribution systems based on an hybrid particle swarm optimization with back propagation neural network UR - http://hdl.handle.net/10204/12161 ER - en_ZA
dc.identifier.worklist 25104 en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record