Network intrusion detection system using genetic network programming with support vector machine

Nowadays Internet Services spread all over the world. There are large amount of data present in the internet services. However the internet services increases at the same time intrusions also increases. Network Intrusion Detection Systems are used to detect the intrusions in the network. For efficient Network Intrusion Detection System the preprocessing is most essential. In order to preprocess the dataset Support Vector Machine algorithm is used and that gives the new data model which has been used for creating rules for misuse detection. The dataset can be classified into two datasets; namely positive kernel and negative kernel. Positive Kernel is used for creating the rules. After classifying the dataset, fuzzification is applied to that datset and then the rules has been created by Genetic Network Programming which based on direct graph structure. In the testing phase the system has been used to detect the misuse activities. By combining SVM with Genetic Network Programming increases the performance of the detection rate of the Network Intrusion Detection Model and reduces the false positive rate.

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