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Hyperparameter search based convolution neural network with Bi-LSTM model for intrusion detection system in multimedia big data environment

  • 1174: Futuristic Trends and Innovations in Multimedia Systems Using Big Data, IoT and Cloud Technologies (FTIMS)
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Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In recent years, there is an exponential increase in the growth of the multimedia data, which is being generated from zettabyte to petabyte scale. At the same time, security issues in networks, Internets and organizations are also continues to increase. The process of finding intrusions in such a big data environment is not easier. Different types of intrusion-detection system (IDS) have been presented for diverse kinds of networking attacks, however, many models could be identified unknown attacks. Deep learning (DL) approaches lately employed to large-scale big data analysis for effectual outcome. In this view, this paper presents a new deep learning based hyperparameter search (HPS) convolutional neural network with Bi-directional long short term memory (CBL) model called HPS-CBL for intrusion detection in big data environment. The HPS-CBL model make use of CBL technique for the identification of intrusions in the network. Since the proper tuning of hyperparameters of the CBL network is highly important, the proposed model uses improved genetic algorithm (IGA) for hyperparameter tuning. The proposed HPS-CBL is validated using a UNSW-NB15 dataset and the results are validated under diverse evaluation parameters. The obtained experimental outcome clearly stated the superior nature of the HPS-CBL model over the compared methods by attaining a maximum precision of 99.24%, recall of 98.69%, F-score of 98.97% and accuracy of 98.18% respectively.

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Authors and Affiliations

  1. Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow, Russia, 117997

    Irina V. Pustokhina

  2. Department of Logistics, State University of Management, Moscow, Russia, 109542

    Denis A. Pustokhin

  3. Computer Science and Engineering, Vignan’s Institute of Information Technology (Autonomous), Visakhapatnam, India

    E. Laxmi Lydia

  4. J. C. Bose University of Science and Technology YMCA, Faridabad, Haryana, India

    Puneet Garg

  5. B.M. Institute of Engineering & Technology, Sonepat, Haryana, India

    Amarender Kadian

  6. Federal University of Piauí, Teresina, 64049-550, Brazil

    K. Shankar

Authors
  1. Irina V. Pustokhina
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  2. Denis A. Pustokhin
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  3. E. Laxmi Lydia
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  4. Puneet Garg
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Correspondence to K. Shankar.

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Pustokhina, I.V., Pustokhin, D.A., Lydia, E.L. et al. Hyperparameter search based convolution neural network with Bi-LSTM model for intrusion detection system in multimedia big data environment. Multimed Tools Appl 81, 34951–34968 (2022). https://doi.org/10.1007/s11042-021-11271-7

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  • DOI: https://doi.org/10.1007/s11042-021-11271-7

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