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Enhancing Network Security Via Machine Learning: Opportunities and Challenges

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Handbook of Big Data Privacy

Abstract

Network security can be defined as the act of protecting any given network against threats that may lead to the availability of the network to be compromised. Moreover, we can also add that unauthorized access or even misuse of network-accessible resources are issues that network security must address. Traditional detection techniques are inefficient when dealing with huge amounts of data because their analysis processes are complex and time-consuming. Hence, the use of tools and techniques provided to us through big data can assist in the analysis and storage of data in intrusion detection systems to help reduce both processing and training time. This document presents a review of the work related to network security via machine learning.

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Author information

Authors and Affiliations

  1. School of Engineering, University of Guelph, Guelph, ON, Canada

    Mahdi Amrollahi, Shahrzad Hadayeghparast & Hadis Karimipour

  2. Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Farnaz Derakhshan

  3. Department of Mathematics and Computer Science, Brandon University, Brandon, MB, Canada

    Gautam Srivastava

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  1. Mahdi Amrollahi
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  2. Shahrzad Hadayeghparast
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  3. Hadis Karimipour
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  5. Gautam Srivastava
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Correspondence to Hadis Karimipour .

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Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  2. Cyber Science Lab, School of Computer Science, University of Guelph, Guelph, ON, Canada

    Ali Dehghantanha

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Amrollahi, M., Hadayeghparast, S., Karimipour, H., Derakhshan, F., Srivastava, G. (2020). Enhancing Network Security Via Machine Learning: Opportunities and Challenges. In: Choo, KK., Dehghantanha, A. (eds) Handbook of Big Data Privacy. Springer, Cham. https://doi.org/10.1007/978-3-030-38557-6_8

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  • DOI: https://doi.org/10.1007/978-3-030-38557-6_8

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