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International Symposium on Security in Computing and Communication

SSCC 2016: Security in Computing and Communications pp 450–460Cite as

SocialBot: Behavioral Analysis and Detection

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 625))

Abstract

Bots refer to automated software that have the capability to execute commands on receiving instructions from BotMaster. SocialBots are the bots present in Online Social Network (OSN) which mimic the activities of the real users. They have the capability to automatically perform the basic functionalities offered by the OSN platforms. These socialbots have widespread usage in political campaigning and product marketing, but SocialBots can also been used for the purpose of swaying voters, mounting political attacks, manipulating public opinion, etc. Apart from these, SocialBots posses various security risks, one of which is befriending an OSN user thereby gaining access to personal details such as birthday, email id, phone number, address, etc. Detection of these SocialBots is therefore an important problem to be solved in order to maintain the reputation of OSN. Our work concentrates on behavioral analysis of these SocialBots in the OSN and identifying features to be used to develop a model for detection of these Socialbots using machine learning. The model, thus developed, is further used as a background process to create a web-based tool for detection of SocialBots. In our work, we created a SocialBot to perform behavioral analysis. This SocialBot got a good response from the real users and was able to grab 100+ real followers along with some real interactions in form of retweet, mention and direct messages.

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Notes

  1. 1.

    Number of times users has interacted with the Tweet. This include all clicks anywhere on the tweet [17].

  2. 2.

    When we say “impression”, we mean that a tweet has been delivered to the Twitter stream of a particular account [20].

References

  1. The socialbot network: when bots socialize for fame and money. In: Proceeding ACSAC 2011 Proceedings of the 27th Annual Computer Security Applications Conference, pp. 93–102. ACM, New York (2011)

    Google Scholar 

  2. Wagner, C., Mitter, S., Strohmaier, M., Krner, C.: When social bots attack: modeling susceptibility of users in online social networks. In: MSM (2012)

    Google Scholar 

  3. Boshmaf, Y., Muslukhov, I., Beznosov, K., Ripeanu, M.: Key challenges in defending against malicious socialbots. In: 5th USENIX Workshop on Large Scale Exploits and Emergent Threats LEET 2012 (2014)

    Google Scholar 

  4. Ferrara, E., Varol, O., Davis, C, Menczer, F., Flammini, A.: The Rise of Social Bots

    Google Scholar 

  5. Hwang, T., Pearce, I., Nanis, M.: SocialBots: voices from the fronts. Mag. Interact. 19(2), 38–45 (2012). ACM, New York

    Article  Google Scholar 

  6. Elishar, A., Fire, M., Kagan, D., Elovici, Y.: Organizational intrusion: organization mining using socialbots. In: 2012 International Conference on Social Informatics (SocialInformatics) (2012)

    Google Scholar 

  7. Yazan, B., Muslukhov, I., Beznosov, K., Ripeanu, M.: Design and analysis of a social botnet. Comput. Netw.: Botnet Act.: Anal. Detect. Shutdown 57(2), 556–578 (2013)

    Google Scholar 

  8. FaceBook API. https://developers.facebook.com/docs/graph-api

  9. Steps Toward Tracking and Managing Your Digital Footprint. http://articles10972.rssing.com/chan-30099336/all_p3.html

  10. Facebook has more than 83 million illegitimate accounts. http://www.bbc.com/news/technology-19093078

  11. Bot or Not? http://truthy.indiana.edu/botornot/

  12. Boshmaf, Y., Ripeanu, M., Beznosov, K., Santos-Neto, E.: Thwarting fake OSN accounts by predicting their victims. In: Proceeding AISec 2015 Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security. ACM, New York (2015)

    Google Scholar 

  13. Dickerson, J.P., Kagan, V., Subrahmanian, V.S.: Using Sentiment to Detect Bots on Twitter: Are Humans More Opinionated than Bots?

    Google Scholar 

  14. List of Twitter bots 2015. http://qz.com/572763/the-best-twitter-bots-of-2015/

  15. Twitter. www.twitter.com

  16. Gepettos Army: Creating International Incidents with Twitter Bots. http://www.slideshare.net/gregmarra/gepettos-army-creating-international-incidents-with-twitter-bots

  17. Twitter Analytics. https://analytics.twitter.com/

  18. Tweepy. www.tweepy.org

  19. Django. https://www.djangoproject.com/

  20. Impression. https://unionmetrics.zendesk.com/hc/en-us/articles/201201636-What-do-you-mean-by-Twitter-reach-exposure-and-impressions-

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Author information

Authors and Affiliations

  1. Department of Information Technology, Indira Gandhi Delhi Technical University for Women, New Delhi, India

    Madhuri Dewangan & Rishabh Kaushal

Authors
  1. Madhuri Dewangan
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  2. Rishabh Kaushal
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Corresponding author

Correspondence to Madhuri Dewangan .

Editor information

Editors and Affiliations

  1. IBM ZURICH RESEARCH LABORATORY, IBM ZURICH RESEARCH LABORATORY, RUESCHLIKON, Switzerland

    Peter Mueller

  2. Technology and Management, Indian Institute of Information Technology and Management, Kerala, India

    Sabu M. Thampi

  3. Temple University, New York, New York, USA

    Md Zakirul Alam Bhuiyan

  4. Computer Science, University of Waikato Computer Science, Hamilton, New Zealand

    Ryan Ko

  5. Deakin University , Burwood, Victoria, Australia

    Robin Doss

  6. University of the West of Scotland , Paisley, Glasgow, United Kingdom

    Jose M. Alcaraz Calero

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© 2016 Springer Nature Singapore Pte Ltd.

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Cite this paper

Dewangan, M., Kaushal, R. (2016). SocialBot: Behavioral Analysis and Detection. In: Mueller, P., Thampi, S., Alam Bhuiyan, M., Ko, R., Doss, R., Alcaraz Calero, J. (eds) Security in Computing and Communications. SSCC 2016. Communications in Computer and Information Science, vol 625. Springer, Singapore. https://doi.org/10.1007/978-981-10-2738-3_39

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  • DOI: https://doi.org/10.1007/978-981-10-2738-3_39

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2737-6

  • Online ISBN: 978-981-10-2738-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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