Internet applications spreading and its high usage popularity result in significant increasing of cyber-attacks. Consequently, network security has become a matter of importance and several methods have been developed for these attacks. For this purpose, Intrusion detec
More
Internet applications spreading and its high usage popularity result in significant increasing of cyber-attacks. Consequently, network security has become a matter of importance and several methods have been developed for these attacks. For this purpose, Intrusion detection systems (IDS) are being used to monitor the attacks occurred on computer networks. Data mining Techniques, Machine Learning, Neural networks, Collective Intelligence, Evolutionary algorithms and Statistical methods are some of algorithms which have been used for classification, training and reviewing detection accuracy with analysis based on the standard datasets in Intrusion Detection Systems. In this Paper, the hybrid algorithm is introduced based on decision tree and support vector machine (SVM) using feature selection and decision rules to apply on IDS. The main idea is to use the strengths of both algorithms in order to improve detection, enhance the accuracy and reduce the rate of error detection of the results. In this algorithm, the best features are selected by SVM, afterwards decision tree is used to make decisions and define rules. The results of applying proposed algorithm are analyzed on the standard dataset KDD Cup99. The proposed method guarantees high detection rate which is proved by simulation results.
Manuscript profile
