NDLI: Worm Intrusion Alarm Modeling Based on Network Traffic Character

Content Provider	IEEE Xplore Digital Library
Author	Lu Guang Yu Fei Guangxue Yue Miaoliang Zhu
Copyright Year	2006
Description	Author affiliation: Inst. of Artificial Intelligence, Zhejiang Univ. (Lu Guang; Yu Fei)
Abstract	The research community is interested in finding effective methods to detect network traffic anomalies such as the propagation of a new worm, and to raise alarm in time. In this paper we research the principle that the number of network traffic can affect self-similarity of network traffics, and analyze the variety of self-similarity caused by abnormal network traffic. We propose a network traffic model on normal behaviors of users. An approach, which is applied to determine whether or not abnormal network traffic exists by comparing Hurst parameter with predefined threshold, is also presented. At last, implementation of network worm detecting agent in NP is described. Results of evaluation show that detecting agent performs very well in test-bed
Sponsorship	STEMES/IMSCCS Conference, USA The University of Iowa, USA Zhejing Univ., China Harbin Eng. Univ., China
Starting Page	142
Ending Page	147
File Size	145065
Page Count	6
File Format	PDF
ISBN	0769525814
DOI	10.1109/IMSCCS.2006.287
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer worms Laboratories Telecommunication traffic FCC Intrusion Alarm Self-Similarity Network Traffic Character Information security Traffic control Computer networks Worm IP networks Autocorrelation Artificial intelligence
Content Type	Text
Resource Type	Article

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