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Artificial intelligence enabled intrusion detection systems for cognitive cyber-physical systems in industry 4.0 environment

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Abstract

In recent days, Cognitive Cyber-Physical System (CCPS) has gained significant interest among interdisciplinary researchers which integrates machine learning (ML) and artificial intelligence (AI) techniques. This era is witnessing a rapid transformation in digital technology and AI where brain-inspired computing-based solutions will play a vital role in industrial informatics. The application of CCPS with brain-inspired computing in Industry 4.0 will create a significant impact on industrial evolution. Though the CCPSs in industrial environment offer several merits, security remains a challenging design issue. The rise of artificial intelligence AI techniques helps to address cybersecurity issues related to CCPS in industry 4.0 environment. With this motivation, this paper presents a new AI-enabled multimodal fusion-based intrusion detection system (AIMMF-IDS) for CCPS in industry 4.0 environment. The proposed model initially performs the data pre-processing technique in two ways namely data conversion and data normalization. In addition, improved fish swarm optimization based feature selection (IFSO-FS) technique is used for the appropriate selection of features. The IFSO technique is derived by the use of Levy Flight (LF) concept into the searching mechanism of the conventional FSO algorithm to avoid the local optima problem. Since the single modality is not adequate to accomplish enhanced detection performance, in this paper, a weighted voting based ensemble model is employed for the multimodal fusion process using recurrent neural network (RNN), bi-directional long short term memory (Bi-LSTM), and deep belief network (DBN), depicts the novelty of the work. The simulation analysis of the presented model highlighted the improved performance over the recent state of art techniques interms of different measures.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number (RGP2/209/42).

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Authors and Affiliations

  1. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

    Manal Abdullah Alohali

  2. Department of Computer Science, King Khalid University, Muhayel Aseer, Saudi Arabia

    Fahd N. Al-Wesabi

  3. Faculty of Computer and IT, Sanaa University, Sanaa, Yemen

    Fahd N. Al-Wesabi

  4. Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia

    Anwer Mustafa Hilal

  5. Department of Computer Science and Engineering, HMR Institute of Technology and Management, Delhi, India

    Shalini Goel

  6. Department of Computer Science and Engineering, Maharaja Agarsen Institute of Technology, New Delhi, India

    Deepak Gupta & Ashish Khanna

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  1. Manal Abdullah Alohali
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  2. Fahd N. Al-Wesabi
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  3. Anwer Mustafa Hilal
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  4. Shalini Goel
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  5. Deepak Gupta
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Correspondence to Fahd N. Al-Wesabi.

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The authors declare that they have no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

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Alohali, M.A., Al-Wesabi, F.N., Hilal, A.M. et al. Artificial intelligence enabled intrusion detection systems for cognitive cyber-physical systems in industry 4.0 environment. Cogn Neurodyn 16, 1045–1057 (2022). https://doi.org/10.1007/s11571-022-09780-8

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  • DOI: https://doi.org/10.1007/s11571-022-09780-8

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