Estimation and application of social network behaviour from network traffic

PhD Thesis


Albdair, Mostfa Mohsin. 2018. Estimation and application of social network behaviour from network traffic. PhD Thesis Doctor of Philosophy. University of Southern Queensland. https://doi.org/10.26192/5f62f10677b5e
Title

Estimation and application of social network behaviour from network traffic

TypePhD Thesis
Authors
AuthorAlbdair, Mostfa Mohsin
SupervisorAddie, Ron
Kist, Alexander
Abdulla, Shahab
Institution of OriginUniversity of Southern Queensland
Qualification NameDoctor of Philosophy
Number of Pages202
Year2018
Digital Object Identifier (DOI)https://doi.org/10.26192/5f62f10677b5e
Abstract

Because traffic is predominantly formed by communication between users or between users and servers which communicate with users, network traffic inherently exhibits social networking behaviour. In this thesis the extent of interaction between entities - as identified by their IP addresses - is extracted from massive anonymized Internet trace datasets obtained from the Center for Applied Internet Data Analysis (CAIDA) and analysed in a multiplicity of ways. A key discovery is that the Pareto principle applies to all the key features which have been identified: sources of traffic, destinations of traffic, traffic flows themselves, and also to the abstract sources, destinations, and flows which are identified by spectral analysis of traffic matrices.

Chapter 2 reviews the literature on traffic analysis emphasising social network behaviour, and also the literature on methods for managing Quality of Service in the Internet. In Chapter 3, we infer the behaviour of social networks from O-D pair traffic by using two groups of analysis methods. The first group of methods is based on frequency analysis. The frequency analysis of origin, destination and O-D Pair statistics are found to exhibit heavy tailed behaviour. The second method is a slightly different characterization of social behaviour.

In Chapter 4, traffic matrices are first decomposed into mean and variation about the mean. The mean traffic matrix is then analysed by singular value decomposition. The variation about the mean is analysed separately in two different ways. First, assuming that traffic associated with one O-D pair is uncorrelated with traffic of any other O-D pair, the variation is analysed by singular value decomposition. Secondly, dispensing with the O-D-traffic independence assumption, the covariance matrix of O-D traffic is analysed by singular value decomposition. It is discovered that in all three cases, the Pareto principal, that a small proportion of eigenvalues explains a high proportion of the matrices, is found to be true. The significance of the covariance eigenflows was tested by simulating a network with independently distributed O-D traffics of on-off type, with Pareto distributed on and off periods. The simulation confirmed the significance of the covariance eigenflows. A new software system comprising more than 7,000 lines of C++ code for analysis of very large pcap trace files, called Antraff, has been developed to carry out all the analysis procedures in Chapters 3 and 4.

In Chapter 5, the understanding of the social-network behaviour exhibited by traffic is applied to design traffic control procedures which have highly significant advantages for maintaining QoS in the Internet. An architecture for protecting QoS is introduced, based on the understanding of social behaviour exhibited by traffic. Whereas DiffServ enables different treatment to be given to different traffic types, in this architecture, known as DefServ, different treatment is given on the basis of the traffic situation.

Keywordsinternet traffic, QoS, social network, traffic matrix, Pareto I
ANZSRC Field of Research 2020460609. Networking and communications
Byline AffiliationsSchool of Agricultural, Computational and Environmental Sciences
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Related outputs

Inference of Social Network Behavior from Internet Traffic Traces
Albdair, Mostfa, Addie, Ronald G. and Fatseas, David. 2016. "Inference of Social Network Behavior from Internet Traffic Traces." 26th International Telecommunication Networks and Applications Conference (ITNAC 2016). Dunedin, New Zealand 07 - 09 Dec 2016 United States. https://doi.org/10.1109/ATNAC.2016.7878773
Better Service from Understanding the Social Network Behaviour in the Internet
Albdair, Mostfa, Addie, Ronald G. and Kist, Alexander. 2019. "Better Service from Understanding the Social Network Behaviour in the Internet." 2018 IEEE Region 10 Conference (TENCON 2018). Jeju, South Korea 28 - 31 Oct 2018 Piscataway, United States. https://doi.org/10.1109/TENCON.2018.8650301
Antraff traffic analysis software user manual, June 11, 2018
Addie, Ron and Albdair, Mostfa. 2018. Antraff traffic analysis software user manual, June 11, 2018. Toowoomba, Australia. University of Southern Queensland.
Social network behaviour inferred from O-D Pair traffic
Albdair, Mostfa, Addie, Ron and Fatseas, David. 2017. "Social network behaviour inferred from O-D Pair traffic." Australian Journal of Telecommunications and the Digital Economy. 5 (2), pp. 131-150. https://doi.org/10.18080/ajtde.v5n2.106
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