An Efficient Anomaly Detection System Using Featured Histogram and Fuzzy Rule Mining
Anomaly detection is a concept widely applied to numerous domains. A number of techniques are used for finding the anomalous attacks. As the network traffic increases the people need an efficient system to monitor packet analysis of network flow data. Due to this frequent item set mining is foremost problem in field of data mining and knowledge discovery. Frequent item sets are produced from very huge data sets by applying several algorithms like apriori algorithm, partition method, pincer-search algorithm, incremental, border algorithm and many more, which take large computing time to calculate all the frequent item sets.