Anomaly Detection by Using Hybrid Method

Abstract

In this paper a new approach has been designed for Intrusion Detection System (IDS). The detection will be for misuse and anomalies for training and testing data detecting the normal users or attacks users.

The method used in this research is a hybrid method from supervised learning and text recognition field for (IDS). Random Forest algorithm used as a supervised learning method to choose the features and k-Nearest Neighbours is a text recognition algorithm used to detect and classify of the legitimate and illegitimate attack types.

The experimental results have shown that the most accurate results is that obtained by using the proposed method and proved that the proposed method can classify the unknown attacks. The results obtained by using benchmark dataset which are: KDD Cup 1999 dataset.