NDLI: Method for anomaly detection based on classifier with time function

Content Provider	IEEE Xplore Digital Library
Author	Tao Liu Ai-ling Qi Yuan-bin Hou Xin-tan Chang
Copyright Year	2008
Description	Author affiliation: Xi'an Univ. of Sci. & Technol., Xi'an (Tao Liu; Ai-ling Qi; Yuan-bin Hou; Xin-tan Chang)
Abstract	In this paper, a method combining Bayesian statistical model with function of time slicing is presented, which is used for network anomaly detection. By using Bayesian statistical model with time function, the method is intended to find and determine anomaly in the computer network. Combining the advantages of Bayesian theorem when solving uncertain problems with the function whose network traffic change with time, the purpose is to establish anomaly intrusion detection model for the network activity so as to determine the occurrence of network anomaly by discovering the relationship among mass events and classifying network system behavior. It has been proved by a simulation experiment that anomaly behavior will be effectively analyzed by Bayesian statistical model with time slicing.
Starting Page	1
Ending Page	4
File Size	93361
Page Count	4
File Format	PDF
ISBN	9781424417056
DOI	10.1109/ICIT.2008.4608512
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Probability Intrusion detection Mathematical model Security Equations IP networks Time Slicing Network security Anomaly detection Bayesian classifier
Content Type	Text
Resource Type	Article

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