EISM-CPS: An Enhanced Intelligent Security Methodology for Cyber-Physical Systems through Hyper-Parameter Optimization

Zakir Ahmad Sheikh, Yashwant Singh, Sudeep Tanwar (), Ravi Sharma, Florin-Emilian Turcanu and Maria Simona Raboaca ()
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Zakir Ahmad Sheikh: Department of Computer Science and IT, Central University of Jammu, Rahya Suchani, Bagla, Samba Jammu 181143, India
Yashwant Singh: Department of Computer Science and IT, Central University of Jammu, Rahya Suchani, Bagla, Samba Jammu 181143, India
Sudeep Tanwar: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Ravi Sharma: Centre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, P.O. Bidholi Via-Prem Nagar, Dehradun 248007, India
Florin-Emilian Turcanu: Department of Building Services, Faculty of Civil Engineering and Building Services, Technical University of Gheorghe Asachi, 700050 Iași, Romania
Maria Simona Raboaca: National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Rm. Vâlcea, Uz-inei Street, No. 4, P.O. Box 7 Râureni, 240050 Rm. Vâlcea, Romania

Mathematics, 2022, vol. 11, issue 1, 1-16

Abstract: The increased usage of cyber-physical systems (CPS) has gained the focus of cybercriminals, particularly with the involvement of the internet, provoking an increased attack surface. The increased usage of these systems generates heavy data flows, which must be analyzed to ensure security. In particular, machine learning (ML) and deep learning (DL) algorithms have shown feasibility and promising results to fulfill the security requirement through the adoption of intelligence. However, the performance of these models strongly depends on the model structure, hyper-parameters, dataset, and application. So, the developers only possess control over defining the model structure and its hyper-parameters for diversified applications. Generally, not all models perform well in default hyper-parameter settings. Their specification is a challenging and complex task and requires significant expertise. This problem can be mitigated by utilizing hyper-parameter optimization (HPO) techniques, which intend to automatically find efficient learning model hyper-parameters in specific applications or datasets. This paper proposes an enhanced intelligent security mechanism for CPS by utilizing HPO. Specifically, exhaustive HPO techniques have been considered for performance evaluation and evaluation of computational requirements to analyze their capabilities to build an effective intelligent security model to cope with security infringements in CPS. Moreover, we analyze the capabilities of various HPO techniques, normalization, and feature selection. To ensure the HPO, we evaluated the effectiveness of a DL-based artificial neural network (ANN) on a standard CPS dataset under manual hyper-parameter settings and exhaustive HPO techniques, such as random search, directed grid search, and Bayesian optimization. We utilized the min-max algorithm for normalization and SelectKBest for feature selection. The HPO techniques performed better than the manual hyper-parameter settings. They achieved an accuracy, precision, recall, and F1 score of more than 98%. The results highlight the importance of HPO for performance enhancement and reduction of computational requirements, human efforts, and expertise.

Keywords: hyper-parameter optimization; hyper-parameter tuning; cyber-physical systems; machine learning; deep learning; cyber attacks; cyber security; critical infrastructures (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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