A Classy Multifacet Clustering and Fused Optimization Based Classification Methodologies for SCADA Security

Alaa O. Khadidos, Hariprasath Manoharan, Shitharth Selvarajan, Adil O. Khadidos, Khaled H. Alyoubi and Ayman Yafoz
Additional contact information
Alaa O. Khadidos: Department of Information Systems, Faculty of Computing and Information Systems, King Abdulaziz University, Jeddah 22254, Saudi Arabia
Hariprasath Manoharan: Department of Electronics and Communication Engineering, Panimalar Institute of Technology, Poonamallee, Chennai 600123, India
Shitharth Selvarajan: Department of Computer Science & Engineering, Kebri Dehar University, Kebri Dehar P.O. Box 250, Ethiopia
Adil O. Khadidos: Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 22254, Saudi Arabia
Khaled H. Alyoubi: Department of Information Systems, Faculty of Computing and Information Systems, King Abdulaziz University, Jeddah 22254, Saudi Arabia
Ayman Yafoz: Department of Information Systems, Faculty of Computing and Information Systems, King Abdulaziz University, Jeddah 22254, Saudi Arabia

Energies, 2022, vol. 15, issue 10, 1-24

Abstract: Detecting intrusions from the supervisory control and data acquisition (SCADA) systems is one of the most essential and challenging processes in recent times. Most of the conventional works aim to develop an efficient intrusion detection system (IDS) framework for increasing the security of SCADA against networking attacks. Nonetheless, it faces the problems of complexity in classification, requiring more time for training and testing, as well as increased misprediction results and error outputs. Hence, this research work intends to develop a novel IDS framework by implementing a combination of methodologies, such as clustering, optimization, and classification. The most popular and extensively utilized SCADA attacking datasets are taken for this system’s proposed IDS framework implementation and validation. The main contribution of this work is to accurately detect the intrusions from the given SCADA datasets with minimized computational operations and increased accuracy of classification. Additionally the proposed work aims to develop a simple and efficient classification technique for improving the security of SCADA systems. Initially, the dataset preprocessing and clustering processes were performed using the multifacet data clustering model (MDCM) in order to simplify the classification process. Then, the hybrid gradient descent spider monkey optimization (GDSMO) mechanism is implemented for selecting the optimal parameters from the clustered datasets, based on the global best solution. The main purpose of using the optimization methodology is to train the classifier with the optimized features to increase accuracy and reduce processing time. Moreover, the deep sequential long short term memory (DS-LSTM) is employed to identify the intrusions from the clustered datasets with efficient data model training. Finally, the proposed optimization-based classification methodology’s performance and results are validated and compared using various evaluation metrics.

Keywords: supervisory control and data acquisition (SCADA); intrusion detection system (IDS); multifacet data clustering model (MDCM); artificial intelligence; gradient descent spider monkey optimization (GDSMO); deep sequential long short term memory (DS-LSTM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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