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Metaheuristic feature selection with deep learning enabled cascaded recurrent neural network for anomaly detection in Industrial Internet of Things environment

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Abstract

Industrial Internet of Things (IIoT) acts as essential part of the revolutionary transition of conventional industries towards Industry 4.0. By the integration of instruments, sensors, and other industry devices to the Internet, the IIoT enables data analysis, acquisition, and automated control, thereby enhancing the production and performance of the IIoT systems. Owing to the complex IIoT infrastructure, dynamic, large scale, and heterogeneous data, anomaly detection becomes an effective way of ensuring the effective performance of the IIoT. Anomaly detection techniques have been commonly employed for accomplishing security in the IIoT environment. The recent advances of deep learning (DL) models find a way to design effective anomaly detection techniques. With this motivation, this study develops a novel metaheuristic feature selection with DL enabled anomaly detection technique in the IIoT environment, named MFSDL-ADIIoT model. The proposed MFSDL-ADIIoT approach aims to effectively identify and classify the presence of anomalies in the IIoT environment. To accomplish this, the MFSDL-ADIIoT model derives a new deer hunting optimization algorithm based feature selection technique to derive useful subset of features. In addition, cascaded recurrent neural network (CRNN) system is applied for the identification and classification of anomalies. Finally, sparrow search algorithm is utilized to optimally tune the parameters involved in the CRNN model. In order to determine the enhanced detection outcomes of the MFSDL-ADIIoT model, a wide range of simulations was carried out. The extensive comparison study reported the better outcomes of the MFSDL-ADIIoT model over the other recent approaches.

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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, Hyderabad, Telangana, India

    Mummadi Upendra Kumar

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  1. Nenavath Chander
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  2. Mummadi Upendra Kumar
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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. Metaheuristic feature selection with deep learning enabled cascaded recurrent neural network for anomaly detection in Industrial Internet of Things environment. Cluster Comput 26, 1801–1819 (2023). https://doi.org/10.1007/s10586-022-03719-8

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  • DOI: https://doi.org/10.1007/s10586-022-03719-8

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