TY - GEN
T1 - Intrusion detection for water distribution systems based on an hybrid particle swarm optimization with back propagation neural network
AU - Alimi, Oyeniyi Akeem
AU - Ouahada, Khmaies
AU - Abu-Mahfouz, Adnan M.
AU - Rimer, Suvendi
AU - Alimi, Kuburat Oyeranti Adefemi
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/9/13
Y1 - 2021/9/13
N2 - 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.
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.
KW - Back-propagation neural network
KW - Classification
KW - Critical infrastructures
KW - Industrial control systems
KW - Particle swarm optimization
KW - Secure water treatment
KW - Water distribution systems
UR - http://www.scopus.com/inward/record.url?scp=85114050088&partnerID=8YFLogxK
U2 - 10.1109/AFRICON51333.2021.9570951
DO - 10.1109/AFRICON51333.2021.9570951
M3 - Conference contribution
AN - SCOPUS:85114050088
T3 - IEEE AFRICON Conference
BT - Proceedings of 2021 IEEE AFRICON, AFRICON 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE AFRICON, AFRICON 2021
Y2 - 13 September 2021 through 15 September 2021
ER -