An equilibrium optimizer with deep recurrent neural networks enabled intrusion detection in secure cyber-physical systems

E Laxmi Lydia; Chukka Santhaiah; Mohammed Altaf Ahmed; K. Vijaya Kumar; Gyanendra Prasad Joshi; Woong Cho; E Laxmi Lydia; Chukka Santhaiah; Mohammed Altaf Ahmed; K. Vijaya Kumar; Gyanendra Prasad Joshi; Woong Cho

doi:10.3934/math.2024574

AIMS Mathematics

2024, Volume 9, Issue 5: 11718-11734. doi: 10.3934/math.2024574

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An equilibrium optimizer with deep recurrent neural networks enabled intrusion detection in secure cyber-physical systems

1.
Department of Computer Science and Engineering, GMR Institute of Technology, Andhra Pradesh, Rajam 532127, India
2.
Professor Department of CSE, SV College of Engineering, Karakambadi, Tirupati, India
3.
Department of Computer Engineering, College of Computer Engineering & Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
4.
Department of Computer Science and Engineering, GITAM School of Technology, GITAM (Deemed to be University), Visakhapatnam, India
5.
Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
6.
Department of Electronics, Information and Communication Engineering, Kangwon National University, Samcheok 25913, Gangwon State, Republic of Korea

Received: 23 January 2024 Revised: 08 March 2024 Accepted: 12 March 2024 Published: 26 March 2024
MSC : 11T71, 68P25, 94A60

Cyber-physical systems (CPSs) are characterized by their integration of physical processes with computational and communication components. These systems are utilized in various critical infrastructure sectors, including energy, healthcare, transportation, and manufacturing, making them attractive targets for cyberattacks. Intrusion detection system (IDS) has played a pivotal role in identifying and mitigating cyber threats in CPS environments. Intrusion detection in secure CPSs is a critical component of ensuring the integrity, availability, and safety of these systems. The deep learning (DL) algorithm is extremely applicable for detecting cyberattacks on IDS in CPS systems. As a core element of network security defense, cyberattacks can change and breach the security of network systems, and then an objective of IDS is to identify anomalous behaviors and act properly to defend the network from outside attacks. Deep learning (DL) and Machine learning (ML) algorithms are crucial for the present IDS. We introduced an Equilibrium Optimizer with a Deep Recurrent Neural Networks Enabled Intrusion Detection (EODRNN-ID) technique in the Secure CPS platform. The main objective of the EODRNN-ID method concentrates mostly on the detection and classification of intrusive actions from the platform of CPS. During the proposed EODRNN-ID method, a min-max normalization algorithm takes place to scale the input dataset. Besides, the EODRNN-ID method involves EO-based feature selection approach to choose the feature and lessen high dimensionality problem. For intrusion detection, the EODRNN-ID technique exploits the DRNN model. Finally, the hyperparameter related to the DRNN model can be tuned by the chimp optimization algorithm (COA). The simulation study of the EODRNN-ID methodology is verified on a benchmark data. Extensive results display the significant performance of the EODRNN-ID algorithm when compared to existing techniques.
- smart environment,
- cyber-physical system,
- security,
- intrusion detection,
- deep learning
Citation: E Laxmi Lydia, Chukka Santhaiah, Mohammed Altaf Ahmed, K. Vijaya Kumar, Gyanendra Prasad Joshi, Woong Cho. An equilibrium optimizer with deep recurrent neural networks enabled intrusion detection in secure cyber-physical systems[J]. AIMS Mathematics, 2024, 9(5): 11718-11734. doi: 10.3934/math.2024574

Related Papers:

Abstract

Cyber-physical systems (CPSs) are characterized by their integration of physical processes with computational and communication components. These systems are utilized in various critical infrastructure sectors, including energy, healthcare, transportation, and manufacturing, making them attractive targets for cyberattacks. Intrusion detection system (IDS) has played a pivotal role in identifying and mitigating cyber threats in CPS environments. Intrusion detection in secure CPSs is a critical component of ensuring the integrity, availability, and safety of these systems. The deep learning (DL) algorithm is extremely applicable for detecting cyberattacks on IDS in CPS systems. As a core element of network security defense, cyberattacks can change and breach the security of network systems, and then an objective of IDS is to identify anomalous behaviors and act properly to defend the network from outside attacks. Deep learning (DL) and Machine learning (ML) algorithms are crucial for the present IDS. We introduced an Equilibrium Optimizer with a Deep Recurrent Neural Networks Enabled Intrusion Detection (EODRNN-ID) technique in the Secure CPS platform. The main objective of the EODRNN-ID method concentrates mostly on the detection and classification of intrusive actions from the platform of CPS. During the proposed EODRNN-ID method, a min-max normalization algorithm takes place to scale the input dataset. Besides, the EODRNN-ID method involves EO-based feature selection approach to choose the feature and lessen high dimensionality problem. For intrusion detection, the EODRNN-ID technique exploits the DRNN model. Finally, the hyperparameter related to the DRNN model can be tuned by the chimp optimization algorithm (COA). The simulation study of the EODRNN-ID methodology is verified on a benchmark data. Extensive results display the significant performance of the EODRNN-ID algorithm when compared to existing techniques.

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