Election-based optimization algorithm with deep learning-enabled false data injection attack detection in cyber-physical systems

Hend Khalid Alkahtani; Nuha Alruwais; Asma Alshuhail; Nadhem NEMRI; Achraf Ben Miled; Ahmed Mahmud; Hend Khalid Alkahtani; Nuha Alruwais; Asma Alshuhail; Nadhem NEMRI; Achraf Ben Miled; Ahmed Mahmud

doi:10.3934/math.2024731

AIMS Mathematics

2024, Volume 9, Issue 6: 15076-15096. doi: 10.3934/math.2024731

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Election-based optimization algorithm with deep learning-enabled false data injection attack detection in cyber-physical systems

1.
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
2.
Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Saudi Arabia, P.O. Box 22459, Riyadh 11495, Saudi Arabia
3.
Department of Information Systems, College of Computer Sciences & Information Technology, King Faisal University, Saudi Arabia
4.
Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
5.
Department of Computer Science at the College of Science, Northern Border University, Arar, Saudi Arabia
6.
Research Center, Future University in Egypt, New Cairo 11835, Egypt

Received: 25 February 2024 Revised: 30 March 2024 Accepted: 11 April 2024 Published: 26 April 2024
MSC : 11Y40

Cyber-physical systems (CPSs) are affected by cyberattacks once they are more connected to cyberspace. Advanced CPSs are highly complex and susceptible to attacks such as false data injection attacks (FDIA) targeted to mislead the systems and make them unstable. Leveraging an integration of anomaly detection methods, real-time monitoring, and machine learning (ML) algorithms, research workers are developing robust frameworks to recognize and alleviate the effect of FDIA. These methods often scrutinize deviations from predictable system behavior, using statistical analysis and anomaly detection systems to determine abnormalities that can indicate malicious activities. This manuscript offers the design of an election-based optimization algorithm with a deep learning-enabled false data injection attack detection (EBODL-FDIAD) method in the CPS infrastructure. The purpose of the EBODL-FDIAD technique is to enhance security in the CPS environment via the detection of FDIAs. In the EBODL-FDIAD technique, the linear scaling normalization (LSN) approach can be used to scale the input data into valuable formats. Besides, the EBODL-FDIAD system performs ensemble learning classification comprising three classifiers, namely the kernel extreme learning machine (KELM), long short-term memory (LSTM), and attention-based bidirectional recurrent neural network (ABiRNN) model. For optimal hyperparameter selection of the ensemble classifiers, the EBO algorithm can be applied. To validate the enriched performance of the EBODL-FDIAD technique, wide-ranging simulations were involved. The extensive results highlighted that the EBODL-FDIAD algorithm performed well over other systems concerning numerous measures.
- cyber-physical system,
- false data injection attack,
- deep learning,
- election-based optimization,
- ensemble learning
Citation: Hend Khalid Alkahtani, Nuha Alruwais, Asma Alshuhail, Nadhem NEMRI, Achraf Ben Miled, Ahmed Mahmud. Election-based optimization algorithm with deep learning-enabled false data injection attack detection in cyber-physical systems[J]. AIMS Mathematics, 2024, 9(6): 15076-15096. doi: 10.3934/math.2024731

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Abstract

Cyber-physical systems (CPSs) are affected by cyberattacks once they are more connected to cyberspace. Advanced CPSs are highly complex and susceptible to attacks such as false data injection attacks (FDIA) targeted to mislead the systems and make them unstable. Leveraging an integration of anomaly detection methods, real-time monitoring, and machine learning (ML) algorithms, research workers are developing robust frameworks to recognize and alleviate the effect of FDIA. These methods often scrutinize deviations from predictable system behavior, using statistical analysis and anomaly detection systems to determine abnormalities that can indicate malicious activities. This manuscript offers the design of an election-based optimization algorithm with a deep learning-enabled false data injection attack detection (EBODL-FDIAD) method in the CPS infrastructure. The purpose of the EBODL-FDIAD technique is to enhance security in the CPS environment via the detection of FDIAs. In the EBODL-FDIAD technique, the linear scaling normalization (LSN) approach can be used to scale the input data into valuable formats. Besides, the EBODL-FDIAD system performs ensemble learning classification comprising three classifiers, namely the kernel extreme learning machine (KELM), long short-term memory (LSTM), and attention-based bidirectional recurrent neural network (ABiRNN) model. For optimal hyperparameter selection of the ensemble classifiers, the EBO algorithm can be applied. To validate the enriched performance of the EBODL-FDIAD technique, wide-ranging simulations were involved. The extensive results highlighted that the EBODL-FDIAD algorithm performed well over other systems concerning numerous measures.

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