A course in fuzzy systems and control
A course in fuzzy systems and control
Intrusion detection with neural networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
Multivariate Statistical Analysis of Audit Trails for Host-Based Intrusion Detection
IEEE Transactions on Computers
Results of the KDD'99 classifier learning
ACM SIGKDD Explorations Newsletter
Winning the KDD99 classification cup: bagged boosting
ACM SIGKDD Explorations Newsletter
A Multi-Class SLIPPER System for Intrusion Detection
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
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Intrusion Detection System (IDS) detects ongoing intrusive activities in information systems. However, an IDS usually suffers high false alarm especially in a dynamically changing environment, which forces continuous tuning on its detection model to maintain sufficient performance. Currently, the manually tuning work greatly depends on the user to work out and integrate the tuning solution. We have developed an automatically tuning intrusion detection system (ATIDS). The experimental results show that when tuning is not delayed too long, the system can achieve about 20% improvement compared with the system without model tuner. But the user can only control whether the tuning should be performed by sending/blocking feedbacks. To give the user more powerful but intuitive control on the tuning, we develop a fuzzy model tuner, through which the user can tune the model fuzzily but yield much appropriate tuning. The results show the system can achieve about 23% improvement.