A Detailed Analysis of Using Supervised Machine Learning for Intrusion Detection

Ahmed Ahmim, Mohamed Amine Ferrag, Leandros Maglaras (), Makhlouf Derdour and Helge Janicke
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Ahmed Ahmim: University of Larbi Tebessi
Mohamed Amine Ferrag: Guelma University
Leandros Maglaras: De Montfort University
Makhlouf Derdour: University of Larbi Tebessi
Helge Janicke: De Montfort University

A chapter in Strategic Innovative Marketing and Tourism, 2020, pp 629-639 from Springer

Abstract: Abstract Machine learning is more and more used in various fields of the industry, which go from the self driving car to the computer security. Nowadays, with the huge network traffic, machine learning represents the miracle solution to deal with network traffic analysis and intrusion detection problems. Intrusion Detection Systems can be used as a part of a holistic security framework in different critical sectors like oil and gas industry, traffic management, water sewage, transportation, tourism and digital infrastructure. In this paper, we provide a comparative study between twelve supervised machine learning methods. This comparative study aims to exhibit the best machine learning methods relative to the classification of network traffic in specific type of attack or benign traffic, category of attack or benign traffic and attack or benign. CICIDS’2017 is used as data-set to perform our experiments, with Random Forest, Jrip, J48 showing better performance.

Keywords: Intrusion detection system; Machine learning; CICIDS 2017; IDS; Network security (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-36126-6_70

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