The base-rate fallacy and its implications for the difficulty of intrusion detection
CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
Why Discretization Works for Naive Bayesian Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Naive Bayes vs decision trees in intrusion detection systems
Proceedings of the 2004 ACM symposium on Applied computing
Features selection for intrusion detection systems based on support vector machines
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
Layered Approach Using Conditional Random Fields for Intrusion Detection
IEEE Transactions on Dependable and Secure Computing
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Intrusion detection is the process of monitoring and analyzing the events occurring in a computer system in order to detect signs of security problems. In real world environment, the minority intrusion attacks namely R2L and U2R/Data attacks are more dangerous than the majority attacks like Probe and DoS. The present day standalone intrusion detection systems are not effective in detecting the minority attacks. Hence, it is essential to improve the detection performance for the minority intrusions, while maintaining a reasonable overall detection rate. In this paper we propose layered approach for improving the minority attack detection rate without hurting the prediction performance of the majority attacks. The proposed model used Naive Bayes classifier on reduced dataset for each attack class. In this system every layer is separately trained to detect a single type of attack category.