A data mining approach for database intrusion detection
Proceedings of the 2004 ACM symposium on Applied computing
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Modeling network intrusion detection alerts for correlation
ACM Transactions on Information and System Security (TISSEC)
Hybrid Intrusion Detection with Weighted Signature Generation over Anomalous Internet Episodes
IEEE Transactions on Dependable and Secure Computing
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Integrating Intrusion Detection System and Data Mining
UMC '08 Proceedings of the 2008 International Symposium on Ubiquitous Multimedia Computing
A simple and efficient hidden Markov model scheme for host- based anomaly intrusion detection
IEEE Network: The Magazine of Global Internetworking - Special issue title on recent developments in network intrusion detection
Intrusion Detection Based on Data Mining
DASC '09 Proceedings of the 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing
A new network intrusion detection identification model research
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Incremental SVM based on reserved set for network intrusion detection
Expert Systems with Applications: An International Journal
Joint network-host based malware detection using information-theoretic tools
Journal in Computer Virology
Intrusion detection using neural based hybrid classification methods
Computer Networks: The International Journal of Computer and Telecommunications Networking
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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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.