Profiling Users by Modeling Web Transactions

Radek Tomsu; S. Marchal; N. Asokan

doi:10.1109/ICDCS.2017.164

Profiling Users by Modeling Web Transactions

Radek Tomsu, S. Marchal, N. Asokan

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

5 Citations (Scopus)

Abstract

Users of electronic devices, e.g., laptop, smartphone, etc. have characteristic behaviors while surfing the Web. Profiling this behavior can help identify the person using a given device. In this paper, we introduce a technique to profile users based on their web transactions. We compute several features extracted from a sequence of web transactions and use them with one-class classification techniques to profile a user. We assess the efficacy and speed of our method at differentiating 25 synthetic users on a benchmark dataset (from a major security vendor) representing 6 months of web traffic monitoring from a small enterprise network.

Original language	English
Title of host publication	2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)
Editors	Kisung Lee, Ling Liu
Publisher	IEEE
Pages	2399-2404
Number of pages	6
ISBN (Print)	978-1-5386-1791-5
DOIs	https://doi.org/10.1109/ICDCS.2017.164
Publication status	Published - 1 Jun 2017
MoE publication type	A4 Conference publication
Event	International Conference on Distributed Computing Systems - Atlanta, United States Duration: 5 Jun 2017 → 8 Jun 2017 Conference number: 37

Publication series

Name	International Conference on Distributed Computing Systems. Proceedings
Publisher	IEEE Computer Society
Volume	37
ISSN (Print)	1063-6927

Conference

Conference	International Conference on Distributed Computing Systems
Abbreviated title	ICDCS
Country/Territory	United States
City	Atlanta
Period	05/06/2017 → 08/06/2017

Keywords

Internet
behavioural sciences computing
feature extraction
pattern classification
Web traffic monitoring
Web transaction modeling
benchmark dataset
characteristic behaviors
electronic devices
laptop
one-class classification techniques
small enterprise network
smartphone
user profiling
Feature extraction
Kernel
Media
Monitoring
Support vector machines
Uniform resource locators

UN SDGs

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

Access to Document

10.1109/ICDCS.2017.164

https://arxiv.org/abs/1703.09745

Cite this

@inproceedings{ef9a3516f93d44b9afff4b76a5e88671,

title = "Profiling Users by Modeling Web Transactions",

abstract = "Users of electronic devices, e.g., laptop, smartphone, etc. have characteristic behaviors while surfing the Web. Profiling this behavior can help identify the person using a given device. In this paper, we introduce a technique to profile users based on their web transactions. We compute several features extracted from a sequence of web transactions and use them with one-class classification techniques to profile a user. We assess the efficacy and speed of our method at differentiating 25 synthetic users on a benchmark dataset (from a major security vendor) representing 6 months of web traffic monitoring from a small enterprise network.",

keywords = "Internet, behavioural sciences computing, feature extraction, pattern classification, Web traffic monitoring, Web transaction modeling, benchmark dataset, characteristic behaviors, electronic devices, laptop, one-class classification techniques, small enterprise network, smartphone, user profiling, Feature extraction, Kernel, Media, Monitoring, Support vector machines, Uniform resource locators",

author = "Radek Tomsu and S. Marchal and N. Asokan",

year = "2017",

month = jun,

day = "1",

doi = "10.1109/ICDCS.2017.164",

language = "English",

isbn = "978-1-5386-1791-5",

series = "International Conference on Distributed Computing Systems. Proceedings",

publisher = "IEEE",

pages = "2399--2404",

editor = "Kisung Lee and Ling Liu",

booktitle = "2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)",

address = "United States",

note = "International Conference on Distributed Computing Systems, ICDCS ; Conference date: 05-06-2017 Through 08-06-2017",

}

Tomsu, R, Marchal, S & Asokan, N 2017, Profiling Users by Modeling Web Transactions. in K Lee & L Liu (eds), 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). International Conference on Distributed Computing Systems. Proceedings, vol. 37, IEEE, pp. 2399-2404, International Conference on Distributed Computing Systems, Atlanta, Georgia, United States, 05/06/2017. https://doi.org/10.1109/ICDCS.2017.164

Profiling Users by Modeling Web Transactions. / Tomsu, Radek; Marchal, S.; Asokan, N.
2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). ed. / Kisung Lee; Ling Liu. IEEE, 2017. p. 2399-2404 (International Conference on Distributed Computing Systems. Proceedings; Vol. 37).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Profiling Users by Modeling Web Transactions

AU - Tomsu, Radek

AU - Marchal, S.

AU - Asokan, N.

N1 - Conference code: 37

PY - 2017/6/1

Y1 - 2017/6/1

N2 - Users of electronic devices, e.g., laptop, smartphone, etc. have characteristic behaviors while surfing the Web. Profiling this behavior can help identify the person using a given device. In this paper, we introduce a technique to profile users based on their web transactions. We compute several features extracted from a sequence of web transactions and use them with one-class classification techniques to profile a user. We assess the efficacy and speed of our method at differentiating 25 synthetic users on a benchmark dataset (from a major security vendor) representing 6 months of web traffic monitoring from a small enterprise network.

AB - Users of electronic devices, e.g., laptop, smartphone, etc. have characteristic behaviors while surfing the Web. Profiling this behavior can help identify the person using a given device. In this paper, we introduce a technique to profile users based on their web transactions. We compute several features extracted from a sequence of web transactions and use them with one-class classification techniques to profile a user. We assess the efficacy and speed of our method at differentiating 25 synthetic users on a benchmark dataset (from a major security vendor) representing 6 months of web traffic monitoring from a small enterprise network.

KW - Internet

KW - behavioural sciences computing

KW - feature extraction

KW - pattern classification

KW - Web traffic monitoring

KW - Web transaction modeling

KW - benchmark dataset

KW - characteristic behaviors

KW - electronic devices

KW - laptop

KW - one-class classification techniques

KW - small enterprise network

KW - smartphone

KW - user profiling

KW - Feature extraction

KW - Kernel

KW - Media

KW - Monitoring

KW - Support vector machines

KW - Uniform resource locators

U2 - 10.1109/ICDCS.2017.164

DO - 10.1109/ICDCS.2017.164

M3 - Conference article in proceedings

SN - 978-1-5386-1791-5

T3 - International Conference on Distributed Computing Systems. Proceedings

SP - 2399

EP - 2404

BT - 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)

A2 - Lee, Kisung

A2 - Liu, Ling

PB - IEEE

T2 - International Conference on Distributed Computing Systems

Y2 - 5 June 2017 through 8 June 2017

ER -

Profiling Users by Modeling Web Transactions

Abstract

Publication series

Conference

Keywords

UN SDGs

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Cite this