文章基本信息

标题：Statistical Inference and ¦Á-Stable Modeling for Anomaly Detection in Network Traffic
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作者：Ravindra Kumar Gupta ; Gajendra Singh Chandel ; Vijay D. Rughwani 等
期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2012
卷号：1
期号：3
页码：55-61
出版社：Shri Pannalal Research Institute of Technolgy
摘要：ANOMALY detection aims at finding the presence of anomalous patterns in network traffic. Automatic detection of such patterns can provide network administrators with an additional source of information to diagnose network behavior or finding the root cause of network faults. However, as of today, a commonly accepted procedure to decide whether a given traffic trace includes anomalous patterns is not available. Indeed, several approaches to this problem have been reported in the literature. 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. So, in the first stage, traffic data are collected from the network (data acquisition). Second, data are analyzed to extract its most relevant features (data analysis). Third, traffic is classified as normal1 or abnormal (inference); and fourth, the whole approach is validated with various types of traffic anomalies (validation).This project paper aims in detecting two anomaly namely flood & flash crowd anomaly using statistical inference & ¦Á-Stable modeling.
关键词：Traffic analysis; anomaly detection; statistical models; hypothesis ; testing; network performance; network reliability