A DATA MINING ANALYSIS & APPROACH WITH INTRUSIONDETECTION / PREVENTION FROM REAL
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2013, Vol 3, Issue 1
Abstract
Abstract: We propose a mechanism for false positive/negative assessment with multiple IDSs/IPSs to collect FP and FN cases from real-world traffic and statistically analyze these cases. False positives and false negatives happen to every intrusion detection and intrusion prevention system. IDSs/IPSs can identify a normal activity as malicious one, causing a false positive (FP) or malicious traffic as normal, causing a false negative (FN) .To create a pool of traffic traces causing possible FPs and FNs to IDSs using Attack Session Extraction (ASE).Statistically analyze the packet by preprocessing based on protocol for each layer .Based on thatBinary classifiers are generated for each class of event using relevant features for the class using classification algorithm .Binary classifiers are derived from the training sample by considering all classes other than the current class .Analyzing the KDD data set for pattern matching and the effect of combining different classifiers can be explained with the theory of bias-variance decomposition using multi boosting.
Authors and Affiliations
Meenakshi. RM
Keywords
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