DocumentCode :
3772292
Title :
Pippy Search: Anomaly Traffic Clustering
Author :
Lili Yang;Jie Wang;Mansoor Ahmed Khuhro
Author_Institution :
Sch. of Inf. Sci. &
fYear :
2015
Firstpage :
378
Lastpage :
383
Abstract :
Terrible network environment is damaging the critical infrastructure and the interests of internet users. In order to ensure the protection and resilience of attack, it is important to better analyze and observe network traffic for discovering anomaly. This paper presents a clustering algorithm by using network-layer and transport-layer statistical feature to classify anomaly traffic. Experiments with public datasets show the proposed algorithm has a significant effectiveness of traffic clustering quality.
Keywords :
"Clustering algorithms","Classification algorithms","Payloads","Computer crime","Algorithm design and analysis","Internet","Feature extraction"
Publisher :
ieee
Conference_Titel :
Smart City/SocialCom/SustainCom (SmartCity), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/SmartCity.2015.101
Filename :
7463755
Link To Document :
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