Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review

The continued ability to detect malicious network intrusions has become an exercise in scalability, in which data mining techniques are playing an increasingly important role. We survey and categorize the fields of data mining and intrusion detection systems, providing a systematic treatment of meth...

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Main Authors:	Fadi Salo, Mohammadnoor Injadat, Ali Bou Nassif, Abdallah Shami, Aleksander Essex
Format:	Article
Language:	English
Published:	IEEE 2018-01-01
Series:	IEEE Access
Subjects:	Intrusion detection system real-time detection data mining network security
Online Access:	https://ieeexplore.ieee.org/document/8476553/

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spelling	doaj-9cfec37ef4494bb2a6f6b7cd1a64fa8b2021-03-29T20:55:42ZengIEEEIEEE Access2169-35362018-01-016560465605810.1109/ACCESS.2018.28727848476553Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature ReviewFadi Salo0https://orcid.org/0000-0001-6521-6978Mohammadnoor Injadat1Ali Bou Nassif2Abdallah Shami3Aleksander Essex4https://orcid.org/0000-0002-0228-0371Department of Electrical and Computer Engineering, Western University, London, ON, CanadaDepartment of Electrical and Computer Engineering, Western University, London, ON, CanadaDepartment of Electrical and Computer Engineering, Western University, London, ON, CanadaDepartment of Electrical and Computer Engineering, Western University, London, ON, CanadaDepartment of Electrical and Computer Engineering, Western University, London, ON, CanadaThe continued ability to detect malicious network intrusions has become an exercise in scalability, in which data mining techniques are playing an increasingly important role. We survey and categorize the fields of data mining and intrusion detection systems, providing a systematic treatment of methodologies and techniques. We apply a criterion-based approach to select 95 relevant articles from 2007 to 2017. We identified 19 separate data mining techniques used for intrusion detection, and our analysis encompasses rich information for future research based on the strengths and weaknesses of these techniques. Furthermore, we observed a research gap in establishing the effectiveness of classifiers to identify intrusions in modern network traffic when trained with aging data sets. Our review points to the need for more empirical experiments addressing real-time solutions for big data against contemporary attacks.https://ieeexplore.ieee.org/document/8476553/Intrusion detection systemreal-time detectiondata miningnetwork security
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Fadi Salo Mohammadnoor Injadat Ali Bou Nassif Abdallah Shami Aleksander Essex
spellingShingle	Fadi Salo Mohammadnoor Injadat Ali Bou Nassif Abdallah Shami Aleksander Essex Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review IEEE Access Intrusion detection system real-time detection data mining network security
author_facet	Fadi Salo Mohammadnoor Injadat Ali Bou Nassif Abdallah Shami Aleksander Essex
author_sort	Fadi Salo
title	Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review
title_short	Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review
title_full	Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review
title_fullStr	Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review
title_full_unstemmed	Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review
title_sort	data mining techniques in intrusion detection systems: a systematic literature review
publisher	IEEE
series	IEEE Access
issn	2169-3536
publishDate	2018-01-01
description	The continued ability to detect malicious network intrusions has become an exercise in scalability, in which data mining techniques are playing an increasingly important role. We survey and categorize the fields of data mining and intrusion detection systems, providing a systematic treatment of methodologies and techniques. We apply a criterion-based approach to select 95 relevant articles from 2007 to 2017. We identified 19 separate data mining techniques used for intrusion detection, and our analysis encompasses rich information for future research based on the strengths and weaknesses of these techniques. Furthermore, we observed a research gap in establishing the effectiveness of classifiers to identify intrusions in modern network traffic when trained with aging data sets. Our review points to the need for more empirical experiments addressing real-time solutions for big data against contemporary attacks.
topic	Intrusion detection system real-time detection data mining network security
url	https://ieeexplore.ieee.org/document/8476553/
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Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review

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