Spot the hotspot: Wi-Fi hotspot classification from internet traffic

Andrey Finkelshtein; Rami Puzis; Asaf Shabtai; Bronislav Sidik

doi:10.1007/978-3-319-39931-7_23

Spot the hotspot: Wi-Fi hotspot classification from internet traffic

Andrey Finkelshtein, Rami Puzis, Asaf Shabtai, Bronislav Sidik

Department of Software and Information Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The meteoric progress of Internet technologies and PDA (personal digital assistant) devices has made public Wi-Fi hotspots very popular. Nowadays, hotspots can be found almost anywhere: organizations, home networks, public transport systems, restaurants, etc. The Internet usage patterns (e.g. browsing) differ with the hotspot venue. This insight introduces new traffic profiling opportunities. Using machine learning techniques we show that it is possible to infer types of venues that provide Wi-Fi access (e.g., organizations and hangout places) by analyzing the Internet traffic of connected mobile phones. We show that it is possible to infer the user’s current venue type disclosing his/her current context. This information can be used for improving personalized and context aware services such as web search engines or online shops, without the presence on user’s device. In this paper we evaluate venue type inference based on mobile phone traffic collected from 115 college students and analyze their Internet behavior across the different venues types.

Original language	English
Title of host publication	Social, Cultural, and Behavioral Modeling - 9th International Conference, SBP-BRiMS 2016, Proceedings
Editors	Nathaniel Osgood, Kevin S. Xu, David Reitter, Dongwon Lee
Publisher	Springer Verlag
Pages	239-249
Number of pages	11
ISBN (Print)	9783319399300
DOIs	https://doi.org/10.1007/978-3-319-39931-7_23
State	Published - 1 Jan 2016
Event	9th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2016 - Washington, United States Duration: 28 Jun 2016 → 1 Jul 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9708 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2016
Country/Territory	United States
City	Washington
Period	28/06/16 → 1/07/16

Keywords

Classification
Hotspot
Machine learning
Smartphone
Wi-Fi

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-39931-7_23

Cite this

Finkelshtein, A., Puzis, R., Shabtai, A., & Sidik, B. (2016). Spot the hotspot: Wi-Fi hotspot classification from internet traffic. In N. Osgood, K. S. Xu, D. Reitter, & D. Lee (Eds.), Social, Cultural, and Behavioral Modeling - 9th International Conference, SBP-BRiMS 2016, Proceedings (pp. 239-249). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9708 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-39931-7_23

Finkelshtein, Andrey ; Puzis, Rami ; Shabtai, Asaf et al. / Spot the hotspot : Wi-Fi hotspot classification from internet traffic. Social, Cultural, and Behavioral Modeling - 9th International Conference, SBP-BRiMS 2016, Proceedings. editor / Nathaniel Osgood ; Kevin S. Xu ; David Reitter ; Dongwon Lee. Springer Verlag, 2016. pp. 239-249 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Spot the hotspot: Wi-Fi hotspot classification from internet traffic",

abstract = "The meteoric progress of Internet technologies and PDA (personal digital assistant) devices has made public Wi-Fi hotspots very popular. Nowadays, hotspots can be found almost anywhere: organizations, home networks, public transport systems, restaurants, etc. The Internet usage patterns (e.g. browsing) differ with the hotspot venue. This insight introduces new traffic profiling opportunities. Using machine learning techniques we show that it is possible to infer types of venues that provide Wi-Fi access (e.g., organizations and hangout places) by analyzing the Internet traffic of connected mobile phones. We show that it is possible to infer the user{\textquoteright}s current venue type disclosing his/her current context. This information can be used for improving personalized and context aware services such as web search engines or online shops, without the presence on user{\textquoteright}s device. In this paper we evaluate venue type inference based on mobile phone traffic collected from 115 college students and analyze their Internet behavior across the different venues types.",

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author = "Andrey Finkelshtein and Rami Puzis and Asaf Shabtai and Bronislav Sidik",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 9th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2016 ; Conference date: 28-06-2016 Through 01-07-2016",

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Finkelshtein, A, Puzis, R , Shabtai, A & Sidik, B 2016, Spot the hotspot: Wi-Fi hotspot classification from internet traffic. in N Osgood, KS Xu, D Reitter & D Lee (eds), Social, Cultural, and Behavioral Modeling - 9th International Conference, SBP-BRiMS 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9708 LNCS, Springer Verlag, pp. 239-249, 9th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2016, Washington, United States, 28/06/16. https://doi.org/10.1007/978-3-319-39931-7_23

Spot the hotspot: Wi-Fi hotspot classification from internet traffic. / Finkelshtein, Andrey; Puzis, Rami ; Shabtai, Asaf et al.
Social, Cultural, and Behavioral Modeling - 9th International Conference, SBP-BRiMS 2016, Proceedings. ed. / Nathaniel Osgood; Kevin S. Xu; David Reitter; Dongwon Lee. Springer Verlag, 2016. p. 239-249 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9708 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Spot the hotspot

T2 - 9th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2016

AU - Finkelshtein, Andrey

AU - Puzis, Rami

AU - Shabtai, Asaf

AU - Sidik, Bronislav

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016/1/1

Y1 - 2016/1/1

N2 - The meteoric progress of Internet technologies and PDA (personal digital assistant) devices has made public Wi-Fi hotspots very popular. Nowadays, hotspots can be found almost anywhere: organizations, home networks, public transport systems, restaurants, etc. The Internet usage patterns (e.g. browsing) differ with the hotspot venue. This insight introduces new traffic profiling opportunities. Using machine learning techniques we show that it is possible to infer types of venues that provide Wi-Fi access (e.g., organizations and hangout places) by analyzing the Internet traffic of connected mobile phones. We show that it is possible to infer the user’s current venue type disclosing his/her current context. This information can be used for improving personalized and context aware services such as web search engines or online shops, without the presence on user’s device. In this paper we evaluate venue type inference based on mobile phone traffic collected from 115 college students and analyze their Internet behavior across the different venues types.

AB - The meteoric progress of Internet technologies and PDA (personal digital assistant) devices has made public Wi-Fi hotspots very popular. Nowadays, hotspots can be found almost anywhere: organizations, home networks, public transport systems, restaurants, etc. The Internet usage patterns (e.g. browsing) differ with the hotspot venue. This insight introduces new traffic profiling opportunities. Using machine learning techniques we show that it is possible to infer types of venues that provide Wi-Fi access (e.g., organizations and hangout places) by analyzing the Internet traffic of connected mobile phones. We show that it is possible to infer the user’s current venue type disclosing his/her current context. This information can be used for improving personalized and context aware services such as web search engines or online shops, without the presence on user’s device. In this paper we evaluate venue type inference based on mobile phone traffic collected from 115 college students and analyze their Internet behavior across the different venues types.

KW - Classification

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DO - 10.1007/978-3-319-39931-7_23

M3 - Conference contribution

AN - SCOPUS:84990986242

SN - 9783319399300

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Social, Cultural, and Behavioral Modeling - 9th International Conference, SBP-BRiMS 2016, Proceedings

A2 - Osgood, Nathaniel

A2 - Xu, Kevin S.

A2 - Reitter, David

A2 - Lee, Dongwon

PB - Springer Verlag

Y2 - 28 June 2016 through 1 July 2016

ER -

Finkelshtein A, Puzis R , Shabtai A, Sidik B. Spot the hotspot: Wi-Fi hotspot classification from internet traffic. In Osgood N, Xu KS, Reitter D, Lee D, editors, Social, Cultural, and Behavioral Modeling - 9th International Conference, SBP-BRiMS 2016, Proceedings. Springer Verlag. 2016. p. 239-249. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-39931-7_23

Spot the hotspot: Wi-Fi hotspot classification from internet traffic

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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