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Enabling secure and efficient industry 4.0 transformation through trust-authorized anomaly detection in cloud environments with a hybrid AI approach

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Abstract

The swift advancement of next-generation technologies for wireless communication has led to the cloud computing model becoming a more effective technique of data processing. In addition, cloud computing services are used in edge computing, often referred to as fog computing, to address new IoT challenges. Cloud computing is vulnerable to many infiltration flaws as a result of its Identity Server's shaky connection and dispersed communication features. Since distributed cloud computing systems are widely used, the great majority of research has concentrated on vulnerability scanning and defense. Nevertheless, there is still promise for the creation of fresh intrusion defense and detection methods. As a result, employing an edge computing-based topology of the network, this research proposed a novel hybrid optimal intelligence technique for the detection and authorization of anomalous dangerous behaviors in IoT networks. To discover anomalies in edge computing networks for detecting device behavior, a unique Evolving Neuro-Fuzzy Intelligence for Anomaly Detection (EFNI-AD) method is proposed in this research. Additionally, an Improved Cipher Crypto System (ICCS) is developed to ensure the reliability of data-passing edge devices. The edge device functions as a certification authority (CA) for the specified trusted domain. In this proposed ICCS paradigm, the edge node just verifies the certificate once to eliminate the overhead of edge devices, and once confidence has been established, all communication may be conducted utilizing regional credentials. The Lionized African Buffalo Optimization (LABO) also improves the assault detection system and trust authorization. The simulation findings indicate that the proposed methods outperform the current methods in terms of several performance criteria such as Packet delivery time, End-to-End delay, throughput, Routing overhead, Packet loss, Accuracy, processing time, Packet Delivery Ratio, and Recall. The proposed method provides lower end-to-end delay and a higher packet delivery ratio.

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Author information

Authors and Affiliations

  1. Department of Information Technology, B.S Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, 600048, India

    N. Prakash

  2. Department of ISE, Jain University, Bengaluru, Karnataka, India

    J. Vignesh

  3. Deparment of Artificial Intelligence and Data Science, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

    M. Ashwin

  4. Sterck Systems, Chennai, India

    Sudhir Ramadass

  5. Department of Information Technology, VFSTR Deemed to Be University, Vadlamudi, Guntur District, Andhra Pradesh, India

    N. Veeranjaneyulu

  6. Department of Computer Engineering, AISSMS COE, Pune, India

    Shashikant V. Athawale

  7. Intel Corporation, Hillsboro, OR, 97124, USA

    Ananda Ravuri

  8. Department of ECE, Kongu Engineering College, Perundurai, 638060, India

    Balambigai Subramanian

Authors
  1. N. Prakash
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  2. J. Vignesh
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  3. M. Ashwin
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  4. Sudhir Ramadass
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  5. N. Veeranjaneyulu
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  6. Shashikant V. Athawale
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  7. Ananda Ravuri
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  8. Balambigai Subramanian
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Contributions

Dr. NP: Idea conceptualization, correspondence. Dr. JV: Algorithm specialization A: Validation of the results Dr.SR: Algorithm specialization Dr. NV: Writing original draft SVA: Editing AR: Data collection, validation. Dr. BS: Reviewing

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Correspondence to N. Prakash.

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Prakash, N., Vignesh, J., Ashwin, M. et al. Enabling secure and efficient industry 4.0 transformation through trust-authorized anomaly detection in cloud environments with a hybrid AI approach. Opt Quant Electron 56, 251 (2024). https://doi.org/10.1007/s11082-023-05781-x

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