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Homing socialbots: intrusion on a specific organization's employee using Socialbots

Published: 25 August 2013 Publication History

Abstract

One dimension on the Internet, which has gained great popularity in recent years are the online social networks (OSNs). Users all over the globe write, share, and publish personal information about themselves, their friends, and their workplace. In this study we present a method for infiltrating specific users in targeted organizations by using organizational social networks topologies and Socialbots. The targeted organizations, which have been chosen by us, were technology-oriented organizations. Employees from this kind of organization should be more aware of the dangers of exposing private information. An infiltration is defined as accepting a Socialbot's friend request. Upon accepting a Socialbot's friend request, users unknowingly expose information about themselves and their workplace. To infiltrate this we had to use our Socialbots in a sophisticated manner. First, we had to gather information and recognize Facebook users who work in targeted organizations. Afterwards, we chose ten Facebook users from every targeted organization randomly. These ten users were chosen to be the specific users from targeted organizations of which we would like to infiltrate. The Socialbots sent friend requests to all specific users' mutual friends who worked or work in the same targeted organization. The rationale behind this idea was to gain as many mutual friends as possible and through this act increase the probability that our friend requests will be accepted by the targeted users. We tested the proposed method on targeted users from two different organizations. Our method was able to gain a successful percentage of 50% and 70% respectively. The results demonstrate how easily adversaries can infiltrate users they do not know and get full access to personal and valuable information. These results are more surprising when we emphasize the fact that we chose oriented users who should be more aware to the dangers of information leakage for this study on purpose. Moreover, the results indicate once again that users who are interested in protecting themselves should not disclose information in OSNs and should be cautious of accepting friendship requests from unknown persons.

References

[1]
Aron O'Cass, and Tino Fenech, "Webretailing adoption: exploring the nature of internet users Webretailing behaviour", Journal of Retailing and Consumer Services 10 (2003) 81--94.
[2]
Robert E. Wilson, Samuel D. Gosling, and Lindsay T. Graham, "A Review of Facebook Research in the Social Sciences", Perspectives on Psychological Science 7(3) 203--220, 2012
[3]
Danah M. Boyd, and Nicole B. Ellison, "Social Network Sites: Definition, History, and Scholarship", Journal of Computer-Mediated Communication 13 (2008) 210--230.
[4]
Kristie Manning, "The impacts of Online Social Networking and Internet Use on Human Communication and Relationships", 2009.
[5]
Somini Sengupta, "Facebook Delivers an Earnings Letdown", The New York Times, Business Day, Technology, July 26-th, 2012. http://www.nytimes.com/2012/07/27/technology/facebook-reports-a-loss-but-its-revenue-beats-expectations.html?ref=facebookinc
[6]
Facebook statistics, http://newsroom.fb.com/content/default.aspx?NewsAreaId=22
[7]
Yazan Boshmaf, Ildar Muslukhov, Konstantin Beznosov, and Matei Ripeanu, "Design and Analysis of a Social Botnet", July 9, 2012.
[8]
Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon, "What is Twitter, a Social Network or a News Media?", WWW 2010, April 26--30, 2010, Raleigh, North Carolina, USA.
[9]
Michael Fire, Rami Puzis, and Yuval Elovici, "Organization Mining Using Online Social Networks", ACM Transactions on Embedded Computing Systems, Vol. 9, No. 4, Article 39, June 2012.
[10]
Aviad Elyashar, Michael Fire, Dima Kagan, and Yuval Elovici, "Organizational Intrusion: Organization Mining using Socialbots", 2012 ASE International Conference On Cyber Security, Washington D.C., USA.
[11]
Catherine Dwyer, Starr Roxanne Hiltz, and Katia Passerini, "Trust and Privacy Concern Within Social Networking Sites: A Comparison of Facebook and MySpace", Proceedings of the Thirteenth Americas Conference on Information Systems, Keystone, Colorado, USA, August 09--12 2007.
[12]
Duen Horng Chau, Shashank Pandit, Samuel Wang, and Christos Faloutsos, "Parallel Crawling for Online Social Networks", WWW 2007, May 8--12, 2007, Banff, Alberta, Canada.
[13]
Alan Mislove, Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, Bobby Bhattacharjee, "Measurement and Analysis of Online Social Networks", IMC'07, October 24--26, 2007, San Diego, California, USA.
[14]
Jack Lindamood, Raymond Heatherly, Murat Kantarcioglu, and Bhavani Thuraisingham, "Inferring Private Information Using Social Network Data", WWW 2009, April 20--24, 2009, Madrid, Spain.
[15]
Tao Stein, Erdong Chen, and Karan Mangla, "Facebook Immune System", EuroSys Social Network Systems (SNS) 2011, April 10, 2011, Salzburg, Austria.
[16]
Yazan Boshmaf, Ildar Muslukhov, Konstantin Beznosov, and Matei Ripeanu, "The Socialbot Network: When Bots Socialize for Fame and Money ", ACSAC 11 Dec. 5--9, 2011, Orlando, Florida, USA.
[17]
Yazan Boshmaf, Ildar Muslukhov, Konstantin Beznosov, and Matei Ripeanu, "Key Challenges in Defending Against Malicious Socialbots", Proceedings of the 5th USENIX workshop on Large-scale exploits and emergent threats, LEET'12, Berkeley, CA, USA.
[18]
Malik Magdon-Ismail, and Brian Orecchio, "Guard Your Connections: Infiltration of a Trust/Reputation Based Network", WebSci 2012, June 22--24, 2012, Evanston, Illinois, USA.
[19]
Roni Stern, Liron Samama, Rami Puzis, Tal Beja, Zahy Bnaya, and Ariel Felner, "TONIC: Target Oriented Network Intelligence Collection for the SocialWeb", Association for the Advancement of Artificial Intelligence, 2013.
[20]
Emil Protalinski, "Facebook: 8.7 percent are fake users", Cnet, News, Internet & Media, August 1, 2012. http://news.cnet.com/8301-1023_3-57484991-93/facebook-8.7-percent-are-fake-users

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cover image ACM Conferences
ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
August 2013
1558 pages
ISBN:9781450322409
DOI:10.1145/2492517
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]

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Publication History

Published: 25 August 2013

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ASONAM '13
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ASONAM '13: Advances in Social Networks Analysis and Mining 2013
August 25 - 28, 2013
Ontario, Niagara, Canada

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

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  • (2023)Friendship Preference: Scalable and Robust Category of Features for Social Bot DetectionIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.315900720:2(1516-1528)Online publication date: 1-Mar-2023
  • (2023)Uneven Hybrid Clickstream Generation Based on Multi-Layer Perceptron2023 Eleventh International Conference on Advanced Cloud and Big Data (CBD)10.1109/CBD63341.2023.00051(247-254)Online publication date: 18-Dec-2023
  • (2021)Analyzing Social Media Research: A Data Quality and Research Reproducibility PerspectiveIIM Kozhikode Society & Management Review10.1177/2277975221101181012:1(39-49)Online publication date: 26-May-2021
  • (2020)Finding Automated (Bot, Sensor) or Semi-Automated (Cyborg) Social Media Accounts Using Network Analysis and NodeXL BasicRobotic Systems10.4018/978-1-7998-1754-3.ch060(1250-1289)Online publication date: 2020
  • (2020)The Chameleon Attack: Manipulating Content Display in Online Social MediaProceedings of The Web Conference 202010.1145/3366423.3380165(848-859)Online publication date: 20-Apr-2020
  • (2020)Quantifying Privacy Vulnerability to Socialbot Attacks: An Adaptive Non-Submodular ModelIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2018.28404338:3(855-868)Online publication date: 1-Jul-2020
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  • (2019)Adaptive Crawling with Cautious Users2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS.2019.00125(1243-1252)Online publication date: Jul-2019
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