Anomaly Extraction in Backbone Networks using Mining Rules

The Progress in anomaly extraction in network backbone , the networking demand for finding out anomaly is growing that also increases demand for finding out root-cause findings, network forensics, attack mitigation, and anomaly modeling especially in huge backbone network. Also when its scope enlarges to rich data traffic and very small number of false positive there will be need to maintain best method for mining. In existing Techniques have been developed for anomaly extraction and data mining and purpose of this paper is to categorize and evaluate these mining methods. For finding out one that is abnormal or exception the best method will be discussed. In my Proposed work Also Summarizes several methods to ensure highly extracted data which includes Apriori algorithm, Frequent Pattern growth algorithm, Enhance FP growth algorithm such a mining methods , Also with the rich traffic data and false positive rate of methods with Several scenarios will discussed.