Inference of Social Network Behavior from Internet Traffic Traces
Paper
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
| Paper/Presentation Title | Inference of Social Network Behavior from Internet Traffic Traces |
|---|---|
| Presentation Type | Paper |
| Authors | Albdair, Mostfa (Author), Addie, Ronald G. (Author) and Fatseas, David (Author) |
| Journal or Proceedings Title | Proceedings of the 2016 26th International Telecommunication Networks and Applications Conference (ITNAC) |
| Number of Pages | 6 |
| Year | 2016 |
| Place of Publication | United States |
| ISBN | 9781509009206 |
| 9781509009190 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ATNAC.2016.7878773 |
| Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/7878773 |
| Conference/Event | 26th International Telecommunication Networks and Applications Conference (ITNAC 2016) |
| Event Details | 26th International Telecommunication Networks and Applications Conference (ITNAC 2016) Event Date 07 to end of 09 Dec 2016 Event Location Dunedin, New Zealand |
| Abstract | All network traffic is a byproduct of social networking. In this paper, Anonymized Internet (IP) Trace Datasets obtained from the Center for Applied Internet Data Analysis (CAIDA) has been used to identify and estimate characteristics of the underlying social network from the overall traffic. The analysis methods used here fall into two groups, the first being based on frequency analysis and second method being based on the use of traffic matrices, with the later analysis method being further sub-divided into groups based on the traffic mean, variance and covariance. The frequency analysis of origin (O), destination (D) and O-D Pair statistics exhibit heavy tailed behavior. Because the large number of IP addresses contained in the CAIDA Datasets, only the most predominate IP Addresses are used when estimating all three sub-divided groups of traffic matrices. Principal Component Analysis (PCA) and related methods are applied to identify key features of each type of traffic matrix. A new system called Antraff has been developed to carry out all the analysis procedures. |
| Keywords | IP networks, Covariance matrices, Matrix decomposition, Social network services, Principal component analysis, Internet, Software |
| ANZSRC Field of Research 2020 | 460609. Networking and communications |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
| Institution of Origin | University of Southern Queensland |
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