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Machine Learning-Based Network Intrusion Detection System

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Computer Networks and Inventive Communication Technologies

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 75))

Abstract

As the network is dramatically extended, security has become a significant issue. Various attacks like DoS, R2L, U2R are significantly increasing to affect these networks. Thus, detecting such intrusions or attacks is a major concern. Intrusions are the activities that breach the system's security policy. The paper's objective is to detect malicious network traffic using machine learning techniques by developing an intrusion detection system in order to provide a more secure network. This paper intends to highlight the performance comparison of various machine learning algorithms like SVM, K-Means Clustering, KNN, Decision tree, Logistic Regression, and Random Forest for the detection of malicious attacks based on their detection accuracies and precision score. A detailed analysis of the network traffic features and the experimental results reveal that Logistic Regression provides the most accurate results.

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Authors and Affiliations

  1. Delhi Technological University, New Delhi, India

    Sumedha Seniaray & Rajni Jindal

Authors
  1. Sumedha Seniaray
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  2. Rajni Jindal
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Corresponding author

Correspondence to Rajni Jindal .

Editor information

Editors and Affiliations

  1. Department of Information Technology, RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

  2. Department of Telecommunication Engineering, Czech Technical University in Prague, Praha, Czech Republic

    Robert Bestak

  3. Gerald Schwartz School of Business, St. Francis Xavier University, Antigonish, NS, Canada

    Ram Palanisamy

  4. Faculty of Informatics and Information Technology, Slovak University Technology, Bratislava, Slovakia

    Ivan Kotuliak

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Seniaray, S., Jindal, R. (2022). Machine Learning-Based Network Intrusion Detection System. In: Smys, S., Bestak, R., Palanisamy, R., Kotuliak, I. (eds) Computer Networks and Inventive Communication Technologies . Lecture Notes on Data Engineering and Communications Technologies, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-16-3728-5_13

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  • DOI: https://doi.org/10.1007/978-981-16-3728-5_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-3727-8

  • Online ISBN: 978-981-16-3728-5

  • eBook Packages: EngineeringEngineering (R0)

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