Anomaly Detection and Prevention in Network Traffic based on Statistical approach and α-Stable Model
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2012, Vol 1, Issue 4
Abstract
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. Finally, the attacks are appraised against principle related to their characteristics, technique and collision. This paper discusses a statistical approach to analysis the distribution of network traffic to recognize the normal network traffic behavior The Research proposals in anomaly detection typically follow a four-stage approach, in which the first three stages define the detection method, while the last stage is dedicated to validate the approach method to detect anomalies in network traffic, based on a non restricted α -stable first-order model and statistical hypothesis testing. Here we focus on detecting and preventing two anomaly types, namely floods and flash-crowd .Here we use NS2 simulator to calculate result.
Authors and Affiliations
Anup Bhange , Sumit Utareja
Keywords
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