Online Detection of Network Traffic Anomalies Using Degree Distributions

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International Journal of Communications, Network and System Sciences

ISSN Print: 1913-3715
ISSN Online: 1913-3723
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"Online Detection of Network Traffic Anomalies Using Degree Distributions"
written by Wuzuo WANG, Weidong WU,
published by International Journal of Communications, Network and System Sciences, Vol.3 No.2, 2010

has been cited by the following article(s):

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[1]	Ataki na urządzenia mobilne i metody ich wykrywania Cybersecurity and Law, 2023 DOI:10.35467/cal/169303

[2]	Deep IP flow inspection to detect beyond network anomalies Computer Communications, 2017 DOI:10.1016/j.comcom.2016.12.007

[3]	Smart grid communication network traffic anomaly detection based on entropy analysis 2016 2nd IEEE International Conference on Computer and Communications (ICCC), 2016 DOI:10.1109/CompComm.2016.7924871

[4]	A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks Information Sciences, 2015 DOI:10.1016/j.ins.2014.09.025

[5]	An Optimum-Path Forest framework for intrusion detection in computer networks Engineering Applications of Artificial Intelligence, 2012 DOI:10.1016/j.engappai.2012.03.008

[6]	Intrusion detection system using Optimum-Path Forest 2011 IEEE 36th Conference on Local Computer Networks, 2011 DOI:10.1109/LCN.2011.6115182

[7]	Entropy based SYN flooding detection 2011 IEEE 36th Conference on Local Computer Networks, 2011 DOI:10.1109/LCN.2011.6115171

[8]	Detection of Attackers in Services Using Anomalous Host Behavior Based on Traffic Flow Statistics 2011 IEEE/IPSJ International Symposium on Applications and the Internet, 2011 DOI:10.1109/SAINT.2011.68

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