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
Parzen-Window Network Intrusion Detectors
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Information-Theoretic Measures for Anomaly Detection
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Pattern synthesis using fuzzy partitions of the feature set for nearest neighbor classifier design
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
| Hi-index | 0.00 |
The problem of detecting anomalous network connections caused by intrusion activities is called Network intrusion detection. Conventional classification methods use data from both normal and intrusion classes to build the classifiers. However, intrusion data are usually scarce and difficult to collect. Novelty detection approach overcomes this problem which depends only on normal data. For this purpose, nonparametric density estimation approaches based on Parzen-window estimators are proposed earlier. Two fundamental problems faced are, (i) due to curse of dimensionality, for high dimensional data with a limited training set, the estimation can be biased and (ii) high computational requirements. We propose, (i) a novel pattern synthesis technique to synthesize artificial new training patterns to increase the training set size and thus to reduce the curse of dimensionality effect, and (ii) a compact data representation scheme to store the entire synthetic set to reduce the computational costs. The effectiveness of our methods are experimentally demonstrated.