NDLI: Adaptive Network Flow Clustering

Content Provider	IEEE Xplore Digital Library
Author	Sui Song Zhixiong Chen
Copyright Year	2007
Description	Author affiliation: New Jersey Inst. of Technol., Newark (Sui Song)
Abstract	Flow level measurements are used to provide insights into the traffic flow crossing a network link. However, existing flow based network detection devices lack adaptive reconfigure functions when facing large number of flow sources such as spoofed attacks. The cache memory for storing flow records and the CPU for processing and/or exporting them could be increasing dramatically beyond what are available. The static sampling technique could not alleviate the issue totally. Instead it missed the ability to log accurately network traffic information. In this paper, we use Fuzzy Logic to achieve adaptive flow clustering. It reacts to the abrupt changes of flow numbers caused by flooding attack or any other attacks, and suggests a best clustering level. Therefore, large amount of flows are aggregated into a few flows in a real time. Our experiments demonstrate that the adaptive flow clustering prevents huge amount of malicious flows from exhausting memories and CPU resources while guarantees the legitimate flows.
Starting Page	596
Ending Page	601
File Size	648070
Page Count	6
File Format	PDF
ISBN	1424410754
DOI	10.1109/ICNSC.2007.372846
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-15
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive systems Telecommunication traffic Traffic control Programmable control Adaptive control Monitoring Communication system traffic control Fuzzy logic Sampling methods Detection algorithms Adaptive Flow Clustering Network Trafric Monitoring Traffic Flow Flow aggregation Scheme
Content Type	Text
Resource Type	Article

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