Anomaly Extraction Using Histogram-Based Detector

Abstract

Now a day’s network traffic monitoring and performance of the network are more important aspect in the computer science. Anomaly Extraction is a method of detecting in large set of flow observed during an anomalous time interval, the flows associated with the one or more anomalous event. Anomaly extraction is important problem that essential for application ranging from root cause analysis and attack mitigation and anomaly extraction is also important problem for several application of testing anomaly detector. In this paper, use a meta-data provided by histogram detector for detect and identify the suspicious flow after successfully detection suspicious flow then applying the association rule mining for finding the anomalous flow. By using the rich traffic data from the meta-data of the histogram-based detector we can reduce the classification cost. In this paper, Anomaly extraction method reduce the working time which is required for analyzing alarm, its make system more practically.
DOI: 10.17762/ijritcc2321-8169.150119