Experiments on multistrategy learning by meta-learning
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Artificial Intelligence Review - Special issue on lazy learning
ACM Computing Surveys (CSUR)
Improving intrusion detection performance using keyword selection and neural networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Counter hack: a step-by-step guide to computer attacks and effective defenses
Counter hack: a step-by-step guide to computer attacks and effective defenses
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
An Eye on Network Intruder-Administrator Shootouts
Proceedings of the Workshop on Intrusion Detection and Network Monitoring
Neural Data Mining for Credit Card Fraud Detection
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Network Intrusion Detection Using an Improved Competitive Learning Neural Network
CNSR '04 Proceedings of the Second Annual Conference on Communication Networks and Services Research
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A study in using neural networks for anomaly and misuse detection
SSYM'99 Proceedings of the 8th conference on USENIX Security Symposium - Volume 8
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Editorial: Hybrid learning machines
Neurocomputing
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
Training a neural-network based intrusion detector to recognize novel attacks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this research, we propose two new clustering algorithms, the improved competitive learning network (ICLN) and the supervised improved competitive learning network (SICLN), for fraud detection and network intrusion detection. The ICLN is an unsupervised clustering algorithm, which applies new rules to the standard competitive learning neural network (SCLN). The network neurons in the ICLN are trained to represent the center of the data by a new reward-punishment update rule. This new update rule overcomes the instability of the SCLN. The SICLN is a supervised version of the ICLN. In the SICLN, the new supervised update rule uses the data labels to guide the training process to achieve a better clustering result. The SICLN can be applied to both labeled and unlabeled data and is highly tolerant to missing or delay labels. Furthermore, the SICLN is capable to reconstruct itself, thus is completely independent from the initial number of clusters. To assess the proposed algorithms, we have performed experimental comparisons on both research data and real-world data in fraud detection and network intrusion detection. The results demonstrate that both the ICLN and the SICLN achieve high performance, and the SICLN outperforms traditional unsupervised clustering algorithms.