A Hybrid Approach for Intrusion Detection Based on Machine Learning

Due to the advancement of Internet over last decade, network security has turned out to be one of the important areas of research. Therefore, Intrusion Detection (ID) is widely studied to achieve overall network security success. Aim of Intrusion Detection System (IDS) is to put checks on attacks and provide desirable support for defense management along with information about the intrusion. The several intrusion detection approaches are proposed so far to predict malicious traffic from the network. In this paper, existing techniques of intrusion detection are reviewed and a hybrid approach based on SVM and KNN for the network traffic classification is proposed where KNN will be used for feature extraction and SVM will classify the data in different classes based on extracted features.