Spatiotemporal model for Internet traffic anomalies. Issue 1 (1st March 2014)

• Record Type: Journal Article
• Authors: Bandara, Vidarshana W.; Pezeshki, Ali; Jayasumana, Anura P.
• Is Part Of: IET networks. Volume 3:Issue 1(2014)
• Page Start: 41
• Page End: 53
• Publication Date: 2014-03-01
• Subjects: Internet -- telecommunication traffic -- computer network performance evaluation -- computer network security; spatiotemporal model -- Internet traffic anomalies -- robust network design -- network provisioning -- network performance studies -- network trafflc anomalies -- common random processes -- anomaly property -- global behaviours -- sophisticated networks -- multiscale analysis framework -- real‐time monitoring system -- regional model parameters -- Internet2 trafflc data; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2013.0123
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract : Models for Internet traffic anomalies greatly benefit a range of applications including robust network design, network provisioning and performance studies. A novel approach to analyse and model network traffic anomalies is presented. The proposed approach individually characterises different aspects of anomalies, such as origin, termination, propagation and changes in duration and volume, with common random processes. These characteristics are then integrated into a single model that successfully captures the overall anomaly behaviours. Characterisation of each anomaly property requires only a few parameters, leading to a concise set of parameters for the entire model. Although the model is calibrated with local measurements made at nodes, it successfully represents the global behaviours of anomalies over the network. The proposed model is applicable both at nodal level and at subnet level. This enables hierarchically analysing large and sophisticated networks. Anomalies are analysed using a multi‐scale analysis framework based on which, a real‐time monitoring system that efficiently communicate ongoing anomaly information across the network is developed. The system is also used for learning regional model parameters distributively. Internet2 traffic data is analysed using the framework, and the corresponding model parameters are derived. These results provide insight on the nature of anomalies in networks.