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Fast and Effective Intrusion Detection Using Multi-Layered Deep Learning Networks

Author

Listed:
  • P. Chellammal

    (Department of CSE, J.J. College of Engineering and Technology, Trichy, India)

  • Sheba Kezia Malarchelvi

    (Department of CSE, Saranathan College of Engineering, Trichy, India)

  • K. Reka

    (Department of Computer Science, Cauvery College for Women(Autonomous), Trichy, India)

  • G. Raja

    (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India)

Abstract

The process of intrusion detection usually involves identifying complex intrusion signatures from a huge repository. This requires a complex model that can identify these signatures. This work presents a deep learning based neural network model that can perform effective intrusion detection on network transmission data. The proposed multi-layered deep learning network is composed of multiple hidden processing layers in the network that makes it a deep learning network. Detection using the deep network was observed to exhibit effective performances in detecting the intrusion signatures. Experiments were performed on standard benchmark datasets like KDD CUP 99, NSL-KDD, and Koyoto 2006+ datasets. Comparisons were performed with state-of-the-art models in literature, and the results and comparisons indicate high performances by the proposed algorithm.

Suggested Citation

  • P. Chellammal & Sheba Kezia Malarchelvi & K. Reka & G. Raja, 2022. "Fast and Effective Intrusion Detection Using Multi-Layered Deep Learning Networks," International Journal of Web Services Research (IJWSR), IGI Global, vol. 19(1), pages 1-16, January.
    • Handle: RePEc:igg:jwsr00:v:19:y:2022:i:1:p:1-16
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    References listed on IDEAS

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    1. Ranjit Panigrahi & Samarjeet Borah & Akash Kumar Bhoi & Muhammad Fazal Ijaz & Moumita Pramanik & Yogesh Kumar & Rutvij H. Jhaveri, 2021. "A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets," Mathematics, MDPI, vol. 9(7), pages 1-35, March.
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