Intrusion detection using classification techniques: a comparative study
by Imad Bouteraa; Makhlouf Derdour; Ahmed Ahmim
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 12, No. 1, 2020

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.

Online publication date: Fri, 06-Mar-2020

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