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Designing Scalable Intrusion Detection Systems with Stacking Based Ensemble Learning

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Intelligent Systems Design and Applications (ISDA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 418))

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Abstract

Network Intrusion Detection Systems monitor the network traffic and reports any malicious activity. In this paper, a combination of feature engineering techniques and Ensemble Learning is proposed to build an effective Intrusion Detection System. The zero importance feature selection method is used to extract 23 features. Random forests, Feed Forward Neural Networks and Auto encoders are used as the base models and the predictions from these base models are combined using Extreme Gradient Boosting (XGB). To ensure that the proposed ensemble model is scalable as well, parallel programming is used for parallel computation of class probabilities from each model of the ensemble. The NSL-KDD dataset is used to train our models. To test our models, we use KDD+test dataset. Experimental results show that the proposed ensemble model outperforms several state-of-the-art works. The proposed parallel programming approach decreases the average prediction time of the model ensuring that the model is scalable.

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Author information

Authors and Affiliations

  1. Information Technology, National Institute of Technology Karnataka, Surathkal, India

    A. Sujan Reddy, S. Akashdeep, S. Sowmya Kamath & Bhawana Rudra

Authors
  1. A. Sujan Reddy
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  2. S. Akashdeep
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  3. S. Sowmya Kamath
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  4. Bhawana Rudra
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Corresponding author

Correspondence to A. Sujan Reddy .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  3. Institut für Wirtschaftsinformatik, Fachhochschule Nordwestschweiz, Olten, Switzerland

    Thomas Hanne

  4. Department of Computer Science and information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Federal University of Bahia, Ondina, Brazil

    Tatiane Nogueira Rios

  6. Nantong University, Nantong Shi, Jiangsu, China

    Weiping Ding

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Reddy, A.S., Akashdeep, S., Kamath, S.S., Rudra, B. (2022). Designing Scalable Intrusion Detection Systems with Stacking Based Ensemble Learning. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_80

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