Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Detecting Anomalous and Unknown Intrusions Against Programs
ACSAC '98 Proceedings of the 14th Annual Computer Security Applications Conference
Statistical Traffic Modeling for Network Intrusion Detection
MASCOTS '00 Proceedings of the 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
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Neural fraud detection in credit card operations
IEEE Transactions on Neural Networks
Real-time intrusion detection in ubiquitous networks with a string-based approach
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part IV
Anomaly detection methods in wired networks: a survey and taxonomy
Computer Communications
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In this paper we introduce GAFT (Generalized Anomaly and Fault Threshold), featuring a novel system architecture that is capable of setting, monitoring and detecting generalized thresholds and soft faults proactively and adaptively. GAFT monitors many network parameters simultaneously, analyzes statistically their performance, combines intelligently the individual decisions and derives an integrated result of compliance for each service class. We have carried out simulation experiments of network resource and service deterioration, when increasingly congested in the presence of class-alien traffic, where GAFT combines intelligently, using a neural network classifier, 12 monitored network performance parameter decisions into a unified result. To this end, we tested five different types of neural network classifiers: Perceptron, BP, PBH, Fuzzy ARTMAP, and RBF. Our results indicate that BP and PBH provide more effective classification than the other neural networks. We also stress tested the entire system, which showed that GAFT can reliably detect class-alien traffic with intensity as low as five to ten percent of typical service class traffic.