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SIRT: A distinctive and smart invasion recognition tool (SIRT) for defending IoT integrated ICS from cyber-attacks

Author

Listed:
  • Kavitha, M.S.
  • Sumathy, G.
  • Sarala, B.
  • Hephzipah, J. Jasmine
  • Dhanalakshmi, R.
  • Subha, T.D.

Abstract

With the rise of smart industries, Industrial Control Systems (ICS) has to move from isolated settings to networked environments to meet the objectives of Industry 4.0. Because of the inherent interconnection of these services, systems of this type are more vulnerable to cybersecurity breaches. To protect ICSs from cyberattacks, intrusion detection systems equipped with Artificial Intelligence characteristics have been used to spot unusual system behavior. The main research problem focused on this work is to guarantee ICS security, a variety of security strategies and automated technologies have been established in past literary works. However, the main problems they face include a high proportion of incorrect predictions, longer execution times, more complex system designs, and decreased efficiency. Thus, developing and putting in place a Smart Invasion Recognition Tool (SIRT) to defend critical infrastructure systems against new cyberattacks is the main goal of this project. This system cleans and normalizes the supplied ICS data using a unique preprocessing technique called Variational Data Normalization (VDN). Furthermore, a novel hybrid technique called Frog Leap-based Ant Movement Optimization (FLAMO) is applied to choose the most important and necessary features from normalized industrial data. Furthermore, the methodology of Weighted Bi-directional Gated Recurrent Network (WeBi-GRN) is utilized to precisely distinguish between genuine and malicious samples from information collected by ICS. This work validates and evaluates the performance findings using many assessment indicators and a range of open-source ICS data. According to the study's findings, the proposed SIRT model accurately classifies the different types of assaults from the industrial data with 99 % accuracy.

Suggested Citation

  • Kavitha, M.S. & Sumathy, G. & Sarala, B. & Hephzipah, J. Jasmine & Dhanalakshmi, R. & Subha, T.D., 2024. "SIRT: A distinctive and smart invasion recognition tool (SIRT) for defending IoT integrated ICS from cyber-attacks," International Journal of Critical Infrastructure Protection, Elsevier, vol. 47(C).
    • Handle: RePEc:eee:ijocip:v:47:y:2024:i:c:s1874548224000611
    DOI: 10.1016/j.ijcip.2024.100720
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    References listed on IDEAS

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    1. Lukas-Valentin Herm & Christian Janiesch & Alexander Helm & Florian Imgrund & Adrian Hofmann & Axel Winkelmann, 2023. "A framework for implementing robotic process automation projects," Information Systems and e-Business Management, Springer, vol. 21(1), pages 1-35, March.
    2. Wang, Weiping & Wang, Chunyang & Wang, Zhen & Yuan, Manman & Luo, Xiong & Kurths, Jürgen & Gao, Yang, 2022. "Abnormal detection technology of industrial control system based on transfer learning," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    3. Mazen Gazzan & Frederick T. Sheldon, 2023. "Opportunities for Early Detection and Prediction of Ransomware Attacks against Industrial Control Systems," Future Internet, MDPI, vol. 15(4), pages 1-18, April.
    4. Hani Alshahrani & Attiya Khan & Muhammad Rizwan & Mana Saleh Al Reshan & Adel Sulaiman & Asadullah Shaikh, 2023. "Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network," Sustainability, MDPI, vol. 15(11), pages 1-18, June.
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