An Integrated Intrusion Detection System by Combining SVM with AdaBoost

Abstract

In the Internet, computers and network equipments are threatened by malicious intrusion, which seriously affects the security of the network. Intrusion behavior has the characteristics of fast upgrade, strong concealment and randomness, so that traditional methods of intrusion detection system (IDS) are difficult to prevent the attacks effectively. In this paper, an integrated network intrusion detection algorithm by combining support vector machine (SVM) with AdaBoost was presented. The SVM is used to construct base classifiers, and the AdaBoost is used for training these learning modules and generating the final intrusion detection model by iterating to update the weight of samples and detection model, until the number of iterations or the accuracy of detection model achieves target setting. The effectiveness of the proposed IDS is evaluated using DARPA99 datasets. Accuracy, a criterion, is used to evaluate the detection performance of the proposed IDS. Experimental results show that it achieves better performance when compared with two state-of-the-art IDS.