Ciza Thomas Improving intrusion detection for imbalanced network traffic Security and Communication Networks 6
The issues of base-rate fallacy and accuracy paradox are addressed in this paper. The data-dependent decision fusion architecture, which learns from the data and then appropriately gives weighting to the decisions of various intrusion detection systems, is proposed for reduced false positive rate and improved overall detection rate and, also, the detection rate of minority class types in particular. The proposed technique is demonstrated to outperform other existing fusion techniques such as OR, AND, SVM and ANN.
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