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Enhancing Network Security Via Machine Learning: Opportunities and Challenges

  • Mahdi Amrollahi
  • Shahrzad Hadayeghparast
  • Hadis KarimipourEmail author
  • Farnaz Derakhshan
  • Gautam Srivastava
Chapter
  • 34 Downloads

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.

Keywords

Cyber security Network Machine learning Intrusion detection (IDS) Supervised learning Anomaly detection 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mahdi Amrollahi
    • 1
  • Shahrzad Hadayeghparast
    • 1
  • Hadis Karimipour
    • 1
    Email author
  • Farnaz Derakhshan
    • 2
  • Gautam Srivastava
    • 3
  1. 1.School of EngineeringUniversity of GuelphGuelphCanada
  2. 2.Department of Electrical and Computer EngineeringUniversity of TabrizTabrizIran
  3. 3.Department of Mathematics and Computer ScienceBrandon UniversityBrandonCanada

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