Walks in Cyberspace: Improving Web Browsing and Network Activity Analysis with 3D Live Graph Rendering
Authors: 
Lionel Tailhardat, 
Raphaël Troncy, and 
Yoan ChabotAuthors Info & Claims
Lionel Tailhardat, Raphaël Troncy, and Yoan ChabotAuthors Info & ClaimsApril 2022
Pages 370 - 375
Abstract
Web navigation generates traces that are useful for Web cartography, user behavior analysis (UEBA) and resource allocation planning. However, this data still needs to be interpreted, sometimes enriched and appropriately visualized to reach its full potential. In this paper, we propose to explore the strengths and weaknesses of standard data collection methods such as mining Web browser history and network traffic dumps. We developed the DynaGraph framework that combines classical traces dumping tools with a Web application for live 3D rendering of graph data. We show that mining navigation history provides useful insights but fails to provide real-time analytics and is not easy to deploy. Conversely, mining network traffic dumps appears easy to set up but rapidly fails once the data traffic is encrypted. We show that 3D rendering allows to highlight navigation patterns for a given data sampling rate.
References
[1]
Noam Ben-Asher, Alessandro Oltramari, Robert F. Erbacher, and Cleotilde González. 2015. Ontology-Based Adaptive Systems of Cyber Defense. In STIDS.
[2]
Isabelle Bloch. 2021. Hybrid AI Models: Some Opportunities for Explainability Application to Spatial Reasoning and Image Understanding. https://gitlab.lip6.fr/maudetn/ia2-explainability/-/blob/master/slides/4-slides-bloch.pdf
[3]
Pieter Bonte, Riccardo Tommasini, Emanuele Della Valle, Filip De Turck, and Femke Ongenae. 2018. Streaming MASSIF: Cascading Reasoning for Efficient Processing of IoT Data Streams. Sensors 18 (Nov. 2018), 3832. https://doi.org/10.3390/s18113832
[4]
Rodrigo A. Botafogo and Ben Shneiderman. 1991. Identifying Aggregates in Hypertext Structures. In Proceedings of the Third Annual ACM Conference on Hypertext - HYPERTEXT ’91. ACM Press, San Antonio, Texas, United States, 63–74. https://doi.org/10.1145/122974.122981
[5]
Vannevar Bush. 1945. As We May Think. https://www.theatlantic.com/magazine/archive/1945/07/as-we-may-think/303881/.
[6]
Giovanna Castellano, Anna M. Fanelli, and Maria A. Torsello. 2013. Web Usage Mining: Discovering Usage Patterns for Web Applications. In Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis. Springer Berlin Heidelberg, Berlin, Heidelberg, 75–104. https://doi.org/10.1007/978-3-642-33326-24
[7]
Oscar Corcho, David Chaves-Fraga, Jhon Toledo, Julián Arenas-Guerrero, Carlos Badenes-Olmedo, Mingxue Wang, Hu Peng, Nicholas Burrett, José Mora, and Puchao Zhang. 2021. Oeg-Upm/Devops-Infra: First Official Version. (April 2021). https://doi.org/10.5281/ZENODO.4701264
[8]
Eric Freeman, Elisabeth Robson, Kathy Sierra, and Bert Bates (Eds.). 2004. Head First Design Patterns. O’Reilly, Sebastopol, CA.
[9]
M. Ghijsen, J. van der Ham, P. Grosso, C. Dumitru, H. Zhu, Z. Zhao, and C. de Laat. 2013. A Semantic-Web Approach for Modeling Computing Infrastructures. Computers & Electrical Engineering 39 (2013). https://doi.org/10.1016/j.compeleceng.2013.08.011
[10]
Sharad Goel, Duncan J. Watts, and Daniel G. Goldstein. 2012. The Structure of Online Diffusion Networks. In Proceedings of the 13th ACM Conference on Electronic Commerce - EC ’12. ACM Press, Valencia, Spain, 623. https://doi.org/10.1145/2229012.2229058
[11]
Gerald H, Sitt Min Oo, Gertjan De Mulder, Michiel Derveeuw, Pieter Heyvaert, Wouter Maroy, Vincent Emonet, Kmhaeren, De Ben Meester, and Thomas. 2021. RMLio/RMLStreamer: Release 2.1.1. https://doi.org/10.5281/ZENODO.3887065
[12]
Ismail Harrando, Pasquale Lisena, and Raphaël Troncy. 2021. Apples to Apples: A Systematic Evaluation of Topic Models. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021). INCOMA Ltd., Held Online, 483–493. https://aclanthology.org/2021.ranlp-1.55
[13]
Thomas Kipf. 2020. Deep Learning with Graph-Structured Representations. Ph.D. Dissertation.
[14]
Jon M. Kleinberg. 1999. Authoritative Sources in a Hyperlinked Environment. J. ACM 46, 5 (Sept. 1999), 604–632. https://doi.org/10.1145/324133.324140
[15]
Ryan McConville, Weiru Liu, and Paul Miller. 2015. Vertex Clustering of Augmented Graph Streams. In Proceedings of the 2015 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 109–117. https://doi.org/10.1137/1.9781611974010.13
[16]
F. Pfaender, M. Jacomy, and G. Fouetillou. 2006. Two Visions of the Web: From Globality to Localities. In 2006 2nd International Conference on Information Communication Technologies, Vol. 1. 566–571. https://doi.org/10.1109/ICTTA.2006.1684433
[17]
Clearcode S.A.2020. User Identification. https://adtechbook.clearcode.cc/user-identification/.
[18]
Jeroen van der Ham, Paola Grosso, Ronald van der Pol, Andree Toonk, and Cees de Laat. 2007. Using the Network Description Language in Optical Networks. In 2007 10th IFIP/IEEE International Symposium on Integrated Network Management. IEEE, Munich, Germany, 199–205. https://doi.org/10.1109/INM.2007.374784
[19]
Tianxing Wu, Arijit Khan, Huan Gao, and Cheng Li. 2019. Efficiently Embedding Dynamic Knowledge Graphs. (Oct. 2019). arxiv:1910.06708
Index Terms
- Walks in Cyberspace: Improving Web Browsing and Network Activity Analysis with 3D Live Graph Rendering
Recommendations
Visualizing Network Data
Networks are critical to modern society, and a thorough understanding of how they behave is crucial to their efficient operation. Fortunately, data on networks is plentiful; by visualizing this data, it is possible to greatly improve our understanding. ...
Interactive 3D visualization of highly connected ecological networks on the WWW
SAC '05: Proceedings of the 2005 ACM symposium on Applied computingFood webs describe who eats whom among species within a habitat. These networks depict interlinked food chains and have long been a central ecological paradigm for studying biocomplexity. As food-web data encompasses more species, the network structure ...
Interactive 3D Visualization for Monitoring and Analysis of Geographical Traffic Data of Various Domains
Advances in Visual ComputingAbstractVisual interactive tools are of great importance for monitoring and analysis of geographical data, and, in particular, traffic data. Substantial research effort goes into visualization techniques of various kinds of geography-bound traffic data. ...
Comments
Information & Contributors
Information
Published In
April 2022
1338 pages
ISBN:9781450391306
DOI:10.1145/3487553
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 August 2022
Check for updates
Author Tags
Qualifiers
- Short-paper
- Research
- Refereed limited
Conference
WWW '22
Sponsor:
Acceptance Rates
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 94Total Downloads
- Downloads (Last 12 months)31
- Downloads (Last 6 weeks)1
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format