IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i10p3624-d816162.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/10/3624/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/10/3624/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yadav, Geeta & Paul, Kolin, 2021. "Architecture and security of SCADA systems: A review," International Journal of Critical Infrastructure Protection, Elsevier, vol. 34(C).
    2. 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).
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ali Alzahrani & Theyazn H. H. Aldhyani, 2023. "Design of Efficient Based Artificial Intelligence Approaches for Sustainable of Cyber Security in Smart Industrial Control System," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
    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. Michał Krzykowski, 2021. "Legal Aspects of Cybersecurity in the Energy Sector—Current State and Latest Proposals of Legislative Changes by the EU," Energies, MDPI, vol. 14(23), pages 1-14, November.
    4. Ugochukwu Onyekachi Obonna & Felix Kelechi Opara & Christian Chidiebere Mbaocha & Jude-Kennedy Chibuzo Obichere & Isdore Onyema Akwukwaegbu & Miriam Mmesoma Amaefule & Cosmas Ifeanyi Nwakanma, 2023. "Detection of Man-in-the-Middle (MitM) Cyber-Attacks in Oil and Gas Process Control Networks Using Machine Learning Algorithms," Future Internet, MDPI, vol. 15(8), pages 1-19, August.
    5. Luiz Fernando Ribas Monteiro & Yuri R. Rodrigues & A. C. Zambroni de Souza, 2023. "Cybersecurity in Cyber–Physical Power Systems," Energies, MDPI, vol. 16(12), pages 1-34, June.
    6. Sachin Sharma & Saish Urumkar & Gianluca Fontanesi & Byrav Ramamurthy & Avishek Nag, 2022. "Future Wireless Networking Experiments Escaping Simulations," Future Internet, MDPI, vol. 14(4), pages 1-32, April.
    7. Wang, Wu & Harrou, Fouzi & Bouyeddou, Benamar & Senouci, Sidi-Mohammed & Sun, Ying, 2022. "Cyber-attacks detection in industrial systems using artificial intelligence-driven methods," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).
    8. Sepideh Radhoush & Maryam Bahramipanah & Hashem Nehrir & Zagros Shahooei, 2022. "A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges," Sustainability, MDPI, vol. 14(5), pages 1-16, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3624-:d:816162. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.