Anomaly Detection and Prevention in Network Traffic Based on Statistical Approach and Stable Model
Network traffic anomalies plunk for a huge division of the Internet traffic and conciliation the performance of the network resources. Detecting and diagnosing these threats is a protracted and time overriding task that network operators face daily. During the past years researchers have rigorous their efforts on this problem and projected several apparatus to automate this task. So, recent progress in anomaly detection has allowable to detect new or unknown anomalies by taking benefit of statistical analysis of the traffic. This analysis study on flood attacks and Flash Crowd and their improvement, classifying such attacks as either high-rate flood or low-rate flood.