Modelling of an Intrusion Detection System Using C4.5 Machine Learning Algorithm

Abstract

The increasing growth of wireless networking and new mobile computing devices has caused boundaries between trusted and malicious users to be blurred. The shift in security priorities from the network perimeter to information protection and user resources security is an open area for research which is concerned with the protection of user information’s confidentiality, integrity and availability. Intrusion detection systems are programs or software applications embedded in sophisticated devices to monitor the activities on networks or systems for security, policy or protocol violation or malicious activities detection. In this work, an intrusion detection model was proposed using C4.5 algorithm which was implemented with WEKA tool and RAPID MINER. The model showed good performance when trained and tested with validation techniques. Implementation of the proposed model was conducted on the Network Security Laboratory Knowledge Discovery in Databases (NSL-KDD) dataset, an improved version of KDD 99 dataset, which showed that the proposed model approach has an average detection rate of 99.62% and reduced false alarm rate of 0.38%.

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“…ey proposed an intrusion detection model based on the C4.5 algorithm. e outcomes showed that the typical identification pace of the model is 99.62%, and the misleading positive rate is diminished by 0.38% [6]. Gao et al concentrated on a twoventure harm conclusion structure for warm security frameworks in light of quantile irregular timberland and self-coordinating guide (SOM) brain organization.…”