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Enhanced pelican optimization algorithm with ensemble-based anomaly detection in industrial internet of things environment

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Abstract

Anomaly detection (AD) in the industrial internet of things (IIoT) platform is said to be the major module of security the consistency, safety, and efficacy of industrial procedures. In IIoT, several sensors and devices always gather massive data counts from tools, infrastructure, and equipment. Ensuring the safety of IIoT AD systems is vital for protecting against cyber-attacks. AD and Intrusion detection are required to work in tandem to maintain industrial procedures. Machine learning (ML) and deep learning (DL) approaches generally employed for AD in IIoT. These methods allow automated AD and pattern detection. With this motivation, this study develops an enhanced pelican optimization model with an ensemble voting-based anomaly detection (EPOA-EVAD) approach. Primarily, the SMOTE approach is used for class imbalance data handling. The presented EPOA-EVAD technique integrates the EPOA-based feature selection and ensemble voting classifier to enhance the accuracy and efficiency of AD in IIoT environments. In addition, the group teaching optimization algorithm with SMOTE approach is used for the purpose of class imbalance data handling. EPOA optimally chooses the related features from the complex and high-dimensional IIoT data. For AD, the EPOA-EVAD technique involves an ensemble learning process comprising gated recurrent unit (GRU), bi-directional long short-term memory (BiLSTM), and stacked autoencoder (SAE). Lastly, the seagull optimization (SGO) algorithm fine-tunes the parameters compared with the DL approaches. The simulation values exhibited that the proposed system outperforms standard approaches, offering robust and adaptive AD capabilities in dynamic IIoT settings. The proposed EPOA-EVAD technique contributes to the advancement of AD techniques crucial for maintaining the integrity and efficiency of industrial processes in the era of IIoT.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Osmania University, Hyderabad, Telangana, India

    Nenavath Chander

  2. Department of Computer Science and Engineering, Muffakham Jah College of Engineering & Technology, Affiliated to OU, Hyderabad, Telangana, India

    Mummadi Upendra Kumar

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  1. Nenavath Chander
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The authors confirm contribution to the paper as follows: NC—study conception and design, data collection, analysis and interpretation of results, and manuscript preparation. Dr. MUK reviewed the results and approved the final version of the manuscript.

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Correspondence to Nenavath Chander.

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Chander, N., Upendra Kumar, M. Enhanced pelican optimization algorithm with ensemble-based anomaly detection in industrial internet of things environment. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04303-y

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