The nature of statistical learning theory
The nature of statistical learning theory
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
Pattern Recognition by Self-Organizing Neural Networks
Pattern Recognition by Self-Organizing Neural Networks
Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines
Anomaly Detection over Noisy Data using Learned Probability Distributions
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Masquerade Detection Using Truncated Command Lines
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Learning nonstationary models of normal network traffic for detecting novel attacks
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ADMIT: anomaly-based data mining for intrusions
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A Neural Network Component for an Intrusion Detection System
SP '92 Proceedings of the 1992 IEEE Symposium on Security and Privacy
A Sense of Self for Unix Processes
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Cost-sensitive, scalable and adaptive learning using ensemble-based methods
Cost-sensitive, scalable and adaptive learning using ensemble-based methods
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Data Mining and Knowledge Discovery
Estimating the Support of a High-Dimensional Distribution
Neural Computation
A study in using neural networks for anomaly and misuse detection
SSYM'99 Proceedings of the 8th conference on USENIX Security Symposium - Volume 8
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Investigation of Fuzzy Adaptive Resonance Theory in Network Anomaly Intrusion Detection
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
A decade of Kasabov's evolving connectionist systems: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An ensemble approach for incremental learning in nonstationary environments
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Abstracting audit data for lightweight intrusion detection
ICISS'10 Proceedings of the 6th international conference on Information systems security
An N-Gram and STF-IDF model for masquerade detection in a UNIX environment
Journal in Computer Virology
Inference of network anomaly propagation using spatio-temporal correlation
Journal of Network and Computer Applications
kENFIS: kNN-based evolving neuro-fuzzy inference system for computer worms detection
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Anomaly detection holds great potential for detecting previously unknown attacks. In order to be effective in a practical environment, anomaly detection systems have to be capable of online learning and handling concept drift. In this paper, a new adaptive anomaly detection framework, based on the use of unsupervised evolving connectionist systems, is proposed to address these issues. It is designed to adapt to normal behavior changes while still recognizing anomalies. The evolving connectionist systems learn a subject's behavior in an online, adaptive fashion through efficient local element tuning. Experiments with the KDD Cup 1999 network data and the Windows NT user profiling data show that our adaptive anomaly detection systems, based on Fuzzy Adaptive Resonance Theory (ART) and Evolving Fuzzy Neural Networks (EFuNN), can significantly reduce the false alarm rate while the attack detection rate remains high.