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Implementation of Machine and Deep Learning Algorithms for Intrusion Detection System

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Intelligent Communication Technologies and Virtual Mobile Networks

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 131))

Abstract

The intrusion detection system (IDS) is an important aspect of network security. This research article presents an analysis of machine and deep learning algorithms for intrusion detection systems. The study utilizes the CICIDS2017 dataset that consists of 79 features. Multilayer perceptrons (MLPs) and random forests (RFs) algorithms are implemented. Four features extraction techniques (information gain, extra tree, random forest, and correlation) are considered for experimentation. Two models have been presented, the first one using the machine learning random forest (RF) algorithm and the second using deep learning multilayer perceptron (MLP) algorithm. The increased accuracy has been observed when using the random forest algorithm. The RF algorithm gives the best results for the four feature selection techniques, thus proving that RF is better than MLP. The RF algorithm gives 99.90% accuracy, and 0.068% false positive rate (FPR) with 36 features. Furthermore, the dimensionality of the features has been reduced from 79 to 18 features with an accuracy of 99.70% and FRP of 0.19%.

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Authors and Affiliations

  1. Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India

    Abdulnaser A. Hagar & Bharti W. Gawali

Authors
  1. Abdulnaser A. Hagar
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  2. Bharti W. Gawali
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Correspondence to Abdulnaser A. Hagar .

Editor information

Editors and Affiliations

  1. Department Electronics and Communication Engineering, Francis Xavier Engineering College, Tirunelveli, India

    G. Rajakumar

  2. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ke-Lin Du

  3. San Jose State University, San Jose, CA, USA

    Chandrasekar Vuppalapati

  4. University of Patras, Agrinio, Greece

    Grigorios N. Beligiannis

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Hagar, A.A., Gawali, B.W. (2023). Implementation of Machine and Deep Learning Algorithms for Intrusion Detection System. In: Rajakumar, G., Du, KL., Vuppalapati, C., Beligiannis, G.N. (eds) Intelligent Communication Technologies and Virtual Mobile Networks. Lecture Notes on Data Engineering and Communications Technologies, vol 131. Springer, Singapore. https://doi.org/10.1007/978-981-19-1844-5_1

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  • DOI: https://doi.org/10.1007/978-981-19-1844-5_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1843-8

  • Online ISBN: 978-981-19-1844-5

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