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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Alaa O. Khadidos & Hariprasath Manoharan & Shitharth Selvarajan & Adil O. Khadidos & Khaled H. Alyoubi & Ayman Yafoz, 2022. "A Classy Multifacet Clustering and Fused Optimization Based Classification Methodologies for SCADA Security," Energies, MDPI, vol. 15(10), pages 1-24, May.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3624-:d:816162
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    References listed on IDEAS

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    1. Abou el Kalam, Anas, 2021. "Securing SCADA and critical industrial systems: From needs to security mechanisms," International Journal of Critical Infrastructure Protection, Elsevier, vol. 32(C).
    2. Oyeniyi Akeem Alimi & Khmaies Ouahada & Adnan M. Abu-Mahfouz & Suvendi Rimer & Kuburat Oyeranti Adefemi Alimi, 2021. "A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification," Sustainability, MDPI, vol. 13(17), pages 1-19, August.
    3. Yadav, Geeta & Paul, Kolin, 2021. "Architecture and security of SCADA systems: A review," International Journal of Critical Infrastructure Protection, Elsevier, vol. 34(C).
    4. Sangeetha K. & Shitharth S. & Gouse Baig Mohammed, 2022. "Enhanced SCADA IDS Security by Using MSOM Hybrid Unsupervised Algorithm," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global, vol. 17(2), pages 1-9, March.
    5. Marcio Andrey Teixeira & Tara Salman & Maede Zolanvari & Raj Jain & Nader Meskin & Mohammed Samaka, 2018. "SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach," Future Internet, MDPI, vol. 10(8), pages 1-15, August.
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    Cited by:

    1. Turki Alsuwian & Aiman Shahid Butt & Arslan Ahmed Amin, 2022. "Smart Grid Cyber Security Enhancement: Challenges and Solutions—A Review," Sustainability, MDPI, vol. 14(21), pages 1-21, October.

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