IEEE Transactions on Software Engineering - Special issue on computer security and privacy
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Winning the KDD99 classification cup: bagged boosting
ACM SIGKDD Explorations Newsletter
KDD-99 classifier learning contest LLSoft's results overview
ACM SIGKDD Explorations Newsletter
Naive Bayes vs decision trees in intrusion detection systems
Proceedings of the 2004 ACM symposium on Applied computing
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In this paper, we propose a new approach to detect network attacks. Network connections are first transformed into data points in the feature space we predetermined. With the field concept in physics, we consider each point like an electric charge exerts a force on others around it and therefore forms a field which we call data field. Each incoming data object would obtain an amount of the potential energy from the field, from which we can recognize the class of such object. We evaluated our approach over KDD Cup 1999 data set. Experimental results show most attacks can be correctly discriminated in our data field and the false positive rate is acceptable. Compared with other approaches, our method has the better performance in detection of PROBE and U2R attacks.