Title: Intrusion detection using classification techniques: a comparative study

Authors: Imad Bouteraa; Makhlouf Derdour; Ahmed Ahmim

Addresses: Department of Computer Sciences, Larbi Tebessi University Tebessa, Algeria ' Department of Computer Sciences, Larbi Tebessi University Tebessa, Algeria ' Department of Computer Sciences, Larbi Tebessi University Tebessa, Algeria

Abstract: Today's highly connected world suffers from the increase and variety of cyber-attacks. To mitigate those threats, researchers have been continuously exploring different methods for intrusion detection through the last years. In this paper, we study the use of data mining techniques for intrusion detection. The research intends to compare the performances of classification techniques for intrusion detection. To reach the goal, we involve 74 classification techniques in this comparative study. The study shows that no technique outperforms the others in all situations. However, some classification methods lead to promising results and give clues for further combinations.

Keywords: data mining; classification; network security; intrusion detection; KDD99.

DOI: 10.1504/IJDMMM.2020.105596

International Journal of Data Mining, Modelling and Management, 2020 Vol.12 No.1, pp.65 - 86

Accepted: 08 Oct 2018
Published online: 03 Mar 2020 *

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