Intrusion Detection with Low False Alarms using Decision Tree-based SVM Classifier

Todays, Intrusion Detection Systems (IDS) are considered as key components of security networks. However, high false positive and false negative rates are the important problems of these systems. On the other hand, many of the existing solutions in the articles are restricted to class datasets due to the use of a specific technique, but in real applications they may have multi-variant datasets. With the impetus of the facts, this paper presents a new anomaly based intrusion detection system using J۴۸ Decision Tree, Support Vector Classifier (SVC) and k-means clustering algorithm in order to reduce false alarm rates and enhance the system performance. J۴۸ decision tree algorithm is used to select the best features and optimize the dataset. Also, an SVM classifier and a modified k-means clustering algorithm are used to build a profile of normal and anomalous behaviors of dataset. Simulation results on benchmark NSL-KDD, CICIDS۲۰۱۷ and synthetic datasets confirm that the proposed method has significant performance in comparison with previous approaches.

Keywords:

Intrusion Detection , K-means Clustering , decision tree , Support vector classifier , NSL-KDD dataset