skip to main content
10.1145/3017971.3017977acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccnsConference Proceedingsconference-collections
research-article

A Novel Technique to Characterize Social Network Users: Comparative Study

Published: 26 November 2016 Publication History

Abstract

In the present digital network, where people share critical information through online social networks on a daily basis, it becomes essential to identify security loopholes and vulnerabilities in social networks. Social spam has become inevitable with increasing number of users exploiting it for communication. Spam profiles have become dodgy for the social platform and beyond, since they pollute the network with insignificant and malicious information. This put an adverse impact on economy, politics, and society. In this paper, we contribute along various dimensions. First, we created a large dataset of genuine and spam profiles and exploited it for validation purpose. Second, we exploited trust based feature for anomalous accounts detection. Third, we proposed a unified framework to classify various types of Twitter users. Fourth, results of the proposed work have been compared with three existing state-of-the-art techniques showing the effectiveness of the proposed technique based upon promising feature selection.

References

[1]
A Gautam. Top Microblogging sites list with high pr and best, March 2016. http://mywptips.com/top-microblogging-sites-list/. Accessed on June, 2016.
[2]
http://www.statista.com/topics/1164/social-networks/.
[3]
Candid Wüest. The Risks of Social Networking. Symantec Corporation, 2010. Accessed on June, 2016.
[4]
R Jeyaraman. Fighting spam with BotMaker, August 2014. https://blog.twitter.com/2014/fighting-spam-with-botmaker. Accessed on June, 2016.
[5]
https://support.twitter.com/articles/18311.
[6]
https://support.twitter.com/articles/166337-the-twitter-glossary. Accessed on June, 2016.
[7]
Chao Yang, Robert Harkreader, GuofeiGu., 2013. Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers. IEEE Transactions on Information Forensics and Security, Vol. 8, No. 8, 2013. DOI=10.1109/TIFS.2013.2267732
[8]
Faraz Ahmed, Muhammad Abulaish, 2013. A generic statistical approach for spam detection in Online Social Networks. Computer Communications Journal, Elsevier, pp 1120--1129, 2013. DOI= 10.1016/j.comcom.2013.04.004.
[9]
Marcel Flores, Aleksandar Kuzmanovic, 2013. Searching for Spam: Detecting Fraudulent Accounts via Web Search, Lecture Notes in Computer Science (LNCS), Springer-Verlag Berlin Heidelberg, Vol. 7799, pp 208--217, 2013. DOI=10.1007/978-3-642-36516-4_21.
[10]
Gianvecchio S, Haining Wang, Jajodia S, 2012. Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? Dependable and Secure Computing, IEEE Transactions, Vol 9, Issue 6, pp 811--824, 2012. DOI=10.1109/TDSC.2012.75.
[11]
S Ghosh, B Viswanath, F Kooti, NK Sharma, G Korlam, F Benevenuto, N Ganguly, K PhaniGummadi, 2012. Understanding and combating link farming in the twitter social network. In Proceedings of the 21st international conference on World Wide Web, 2012.
[12]
S Yardi, D Romero, G Schoenebeck, D Boyd, 2010. Detecting spam in a Twitter network. First Monday, Vol 15, No 1, Jan. 2010.
[13]
G Stringhini, C Kruegel, G Vigna, 2010. Detecting Spammers on Social Networks. In Proc. of the 26th Annual Computer Security Applications Conference (ACSAC'10), University of California, Santa Barbara, Austin, Texas USA, ACM, pp 1--9, 2010. DOI=10.1145/1920261.1920263.
[14]
HA Wang, 2010. Don't Follow Me: Spam Detection in Twitter,InProceedings of the 2010 International Conference on Security and Cryptography (SECRYPT), IEEE, pp 1--10, 2010. DOI= 10.5220/0002996201420151.
[15]
K Lee, J Caverlee, S Webb, 2010. Uncovering social spammers: social honeypots + machine learning, In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, July 19--23, 2010, Geneva, Switzerland. DOI=10.1145/1835449.1835522.
[16]
Grace Gee, HaksonTeh, 2010. Twitter Spammer Profile Detection. Available online: cs229.stanford.edu/proj2010/GeeTeh-Twitter Spammer Profile Detection.pdf, 2010.
[17]
Jonghyuk Song, Sangho Lee, Jong Kim, 2011. Spam Filtering in Twitter using Sender-Receiver Relationship. In Proceedings of the 14th International Conference on Recent Advances in Intrusion Detection (RAID'11), Springer-Verlag Berlin, Heidelberg, pp 301--317, 2011.
[18]
M. McCord, M. Chuah, 2011. Spam Detection on Twitter Using Traditional Classifiers. In Proceedings of the 8th international conference on Autonomic and trusted computing (ATC'11),Springer-Verlag Berlin, Heidelberg, pp 175--186, 2011.
[19]
F Benevenuto, G Magno, T Rodrigues, V Almeida, 2010. Detecting Spammers on Twitter. In Proc. of Seventh annual Collaboration, Electronic messaging, Anti Abuse and Spam Conference (CEAS 2010), Washington, US, 2010. DOI=10.1.1.297.5340.
[20]
Leyla Bilge, Thorsten Strufe, DavideBalzarotti, EnginKirda, 2009. All Your Contacts Are Belong to Us: Automated Identity Theft Attacks on Social Networks. In Proceedings of International World Wide Web Conference Committee (IW3C2), WWW 2009, Madrid, Spain, ACM, 2009.
[21]
G Kontaxis, I Polakis, S Ioannidis, EP Markatos, 2011. Detecting Social Network Profile Cloning. In Proc. of International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), Seattle, WA, IEEE, pp 295--300, 2011.
[22]
Z Yang, C Wilson, X Wang X, T Gao, BY Zhao, Dai Yafei, 2011.Uncovering Social Network Sybils in the Wild. In Proc. of the ACM SIGCOMM conference on Internet measurement conference (IMC'11), New York, USA, pp 259--268, 2011.
[23]
M Conti, R Poovendran, M Secchiero, 2012. FakeBook: Detecting Fake Profiles in On-line Social Networks. In Proc. of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Istanbul, pp 1071--1078, 2012.
[24]
Manuel Egele, GianlucaStringhini, Christopher Kruegel, and Giovanni Vigna, 2013. COMPA: Detecting Compromised Accounts on Social Networks. In Proceedings of Network and Distributed System Security Symposium (NDSS), CA, United States, 2013.
[25]
https://dev.twitter.com/rest/public. Accessed on April 2015.
[26]
Monika Singh, Divya Bansal, Sanjeev Sofat, 2016. Behavioral analysis and classification of spammers distributing pornographic content in social media, Social Network Analysis and Mining Journal. (2016).
[27]
http://nodexl.codeplex.com/. Last Accessed on June 2015.
[28]
https://support.twitter.com/articles/64986?lang=en.
[29]
https://developers.google.com/safe-browsing/.
[30]
http://uribl.com/. Accessed on June 2015.
[31]
http://wiki.aa419.org/index.php/Main_Page.
[32]
http://www.surbl.org/. Accessed on June 2015.
[33]
http://spamvertised.abusebutler.com/.
[34]
https://www.spamcop.net/. Accessed on June 2015
[35]
http://www.phishtank.com/. Accessed on June 2015.
[36]
http://www.malwaredomainlist.com/.http://www.joewein.de/sw/blacklist.htm. Accessed on June 2015.
[37]
http://spamassassin.apache.org/. Accessed on June 2015.
[38]
http://www.bruceclay.com/blog/what-is-klout/.
[39]
http://www.socialnomics.net/2012/08/16/klout-score-formula-insights/. Accessed on Oct. 2015.
[40]
http://www.cs.waikato.ac.nz/ml/weka/.
[41]
Z Lu, 2004. Predicting Subcellular Localization of Proteins using Machine-Learned Classifiers. Bioinformatics. 20(4), 547--556, 2004. DOI= 10.1.1.131.4463.

Cited By

View all
  • (2022)A Systematic Literature Mapping on Profile Trustworthiness in Fake News Spread2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD54268.2022.9776232(275-279)Online publication date: 4-May-2022
  • (2021)Methods and Challenges in Social Bots Detection: A Systematic ReviewProceedings of the XVII Brazilian Symposium on Information Systems10.1145/3466933.3466973(1-8)Online publication date: 7-Jun-2021
  • (2020)Machine Learning Models with Optimization for Clothing Recommendation from Personal Wardrobe2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE)10.1109/ICETCE48199.2020.9091777(1-6)Online publication date: Feb-2020

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICCNS '16: Proceedings of the 6th International Conference on Communication and Network Security
November 2016
133 pages
ISBN:9781450347839
DOI:10.1145/3017971
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 the author(s) 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 November 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Compromised accounts
  2. Fake profiles
  3. Machine learning classification
  4. Spammers
  5. Twitter

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICCNS '16

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)2
  • Downloads (Last 6 weeks)0
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2022)A Systematic Literature Mapping on Profile Trustworthiness in Fake News Spread2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD54268.2022.9776232(275-279)Online publication date: 4-May-2022
  • (2021)Methods and Challenges in Social Bots Detection: A Systematic ReviewProceedings of the XVII Brazilian Symposium on Information Systems10.1145/3466933.3466973(1-8)Online publication date: 7-Jun-2021
  • (2020)Machine Learning Models with Optimization for Clothing Recommendation from Personal Wardrobe2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE)10.1109/ICETCE48199.2020.9091777(1-6)Online publication date: Feb-2020

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media