Pippy Search: Anomaly Traffic Clustering
- Resource Type
- Conference
- Authors
- Yang, Lili; Wang, Jie; Khuhro, Mansoor Ahmed
- Source
- 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity) Smart City/SocialCom/SustainCom (SmartCity), 2015 IEEE International Conference on. :378-383 Dec, 2015
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Clustering algorithms
Classification algorithms
Payloads
Computer crime
Algorithm design and analysis
Internet
Feature extraction
anomaly
classify
traffic
statistical feature
cluster
- Language
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.