A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
D-SCIDS: distributed soft computing intrusion detection system
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
A three-tier IDS via data mining approach
Proceedings of the 3rd annual ACM workshop on Mining network data
Attack Grammar: A New Approach to Modeling and Analyzing Network Attack Sequences
ACSAC '08 Proceedings of the 2008 Annual Computer Security Applications Conference
Adaptive Distributed Intrusion Detection Using Parametric Model
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Layered Approach Using Conditional Random Fields for Intrusion Detection
IEEE Transactions on Dependable and Secure Computing
A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering
Expert Systems with Applications: An International Journal
A novel intrusion detection system based on hierarchical clustering and support vector machines
Expert Systems with Applications: An International Journal
Toward credible evaluation of anomaly-based intrusion-detection methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
AdaBoost-Based Algorithm for Network Intrusion Detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In general, the kind of users and the injection of network packets into the internet sectors are not under specific control. There is no clear description as to what packets can be considered normal or abnormal. If the invasions are not detected at the appropriate level, the loss to system may be some times unimaginable. Although many intrusion detection system (IDS) methods are used to detect the existing types of attacks within the network infrastructures, reducing false negative and false positives is still a major issue. In our paper an intrusion detection system is designed to classify by the incorporation of enhanced rules as learnt from the network behavior with less computational complexity of O(n). The method demonstrates the achievements of promising classification rate. The bench mark data KDD Cup99 data is used in our method.