Paper Info
Title
Network intrusion detection system using genetic network programming with support vector machine
Abstract
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.
Year
DOI
Venue
2012
10.1145/2345396.2345501
ICACCI
Keywords
Field
DocType
genetic network programming,network intrusion detection systems,network intrusion detection model,positive kernel,network intrusion detection system,internet service,internet services increase,efficient network intrusion detection,support vector machine,detection rate,dataset support,false positive rate,kernel function,data model,directed graph
Kernel (linear algebra),Data mining,False positive rate,Computer science,Support vector machine,Anomaly-based intrusion detection system,Preprocessor,Artificial intelligence,Misuse detection,Data model,Machine learning,The Internet
Conference
Citations 
PageRank 
References 
0
0.34
11
Authors
3
Name
Order
Citations
PageRank
P. Kola Sujatha110.69
C. Suba Priya200.34
A. Kannan319525.98