Clustered Approach for Clone Detection in Social Media

C. R. Liyanage (1), S. C. Premarathne (2)
(1) Department of ICT, Faculty of Technology, University of Ruhuna, Sri Lanka
(2) Department of IT, Faculty of IT, University of Moratuwa, Sri Lanka
Fulltext View | Download
How to cite (IJASEIT) :
Liyanage, C. R., and S. C. Premarathne. “Clustered Approach for Clone Detection in Social Media”. International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 1, Feb. 2021, pp. 99-104, doi:10.18517/ijaseit.11.1.9272.
With the popularity of Online Social Networks (OSN), the number of different types of digital attacks has been increased causing lots of damages to their users. Identity Clone Attack (ICA) is one of the leading among them which illegally uses the information of a genuine user by duplicating it in another fake profile. These attacks severely affect a true and innocent identity since it can be misused by another malicious profile. Hence these clone profiles need to be identified and removed in order to increase the protection of users. This study introduces a model to detect clone profiles on Facebook by using a dataset that consists of profiles with attributes and network connections. Though the initial dataset was real, it was modified to make some artificial clones. The process of detection included three main stages, namely, filter by name, cluster using weighted categorical attributes and measure the strength of friend relationships among profiles, which follow one after another respectively. Finally, the list of possible clones with their percentages representing the amount of duplicability to a given victim profile was presented as the output of the model. Instead suggesting the exact clones, the representation of this duplicability percentages makes this approach more practical since there are many similar profiles but not clones. With the use of Agglomerative hierarchical clustering algorithm and Jaccard similarity measurement, a low average within cluster distance in cluster density performance and a precision of 88.75% has shown in the results. The present study highly focuses on the distribution of the dataset, where the calculation of weights for the attributes, similarity threshold and even the selection of the clustering algorithm is done based on it and this increases the adjustability of the proposed model to any other dataset. As the future improvements, this newly proposed approach can be extended to find clones of a victim on different platforms and more attributes can be considered for clustering.

Statista, “Social Media Statistics & Facts,” 2017. [Online]. Available: https://www.statista.com/topics/1164/social-networks/. [Accessed: 30-Oct-2017].

WordStream, “40 Essential Social Media Marketing Statistics for 2017,” 2017. [Online]. Available: http://www.wordstream.com/blog/ws/2017/01/05/social-media-marketing-statistics. [Accessed: 10-Nov-2017].

F. Rizi, M. Khayyambashi, and M. Kharaji, “A New Approach for Finding Cloned Profiles in Online Social Networks,” Int. J. Netw. Secur., vol. 6, no. April, pp. 25-37, 2014.

L. Jin, H. Takabi, and J. B. D. Joshi, “Towards active detection of identity clone attacks on online social networks,” Proc. first ACM Conf. Data Appl. Secur. Priv. - CODASPY ’11, p. 27, 2011.

P. Dewan, S. Bagroy, and P. Kumaraguru, “Hiding in Plain Sight: Characterizing and Detecting Malicious Facebook Pages,” pp. 193-196, 2016.

K. Krombholz, D. Merkl, and E. Weippl, “Fake identities in social media: A case study on the sustainability of the Facebook business model,” J. Serv. Sci. Res., vol. 4, no. 2, pp. 175-212, 2012.

G. A. Kamhoua et al., “Preventing Colluding Identity Clone Attacks in Online Social Networks,” in 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW), 2017, pp. 187-192.

M. A. Devmane and N. K. Rana, “Detection and prevention of profile cloning in online social networks,” Int. Conf. Recent Adv. Innov. Eng. ICRAIE 2014, pp. 9-13, 2014.

M. Torky, A. Meligy, and H. Ibrahim, “Recognizing fake identities in online social networks based on a finite automaton approach,” 2016 12th Int. Comput. Eng. Conf. ICENCO 2016 Boundless Smart Soc., pp. 1-7, 2017.

M. Egele, C. Kruegel, and G. Vigna, “COMPA : Detecting Compromised Accounts on Social Networks.”

P. Bródka, M. Sobas, and H. Johnson, “Profile cloning detection in social networks,” Proc. - 2014 Eur. Netw. Intell. Conf. ENIC 2014, pp. 63-68, 2014.

A. Malhotra, L. Totti, W. Meira, P. Kumaraguru, and V. Almeida, “Studying user footprints in different online social networks,” Proc. 2012 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Mining, ASONAM 2012, pp. 1065-1070, 2013.

R. N. Reddy and N. Kumar, “Automatic detection of fake profiles in online social networks,” 2012.

N. Kumar and R. N. Reddy, “Automatic Detection of Fake Profiles in Online Social Networks,” National Institute of Technology Rourkela Rourkela-769 008, Orissa, India, 2012.

M. Kharaji and F. Rizi, “An IAC Approach for Detecting Profile Cloning in Online Social Networks,” Int. J. Netw. Secur. Its Appl., vol. 6, no. 1, pp. 75-90, 2014.

M. Zabielski, R. Kasprzyk, Z. Tarapata, and K. SzkóÅ‚ka, “Methods of Profile Cloning Detection in Online Social Networks,” MATEC Web Conf., vol. 76, 2016.

G. Kontaxis, I. Polakis, S. Ioannidis, and E. P. Markatos, “Detecting social network profile cloning,” 2011 IEEE Int. Conf. Pervasive Comput. Commun. Work. PERCOM Work. 2011, pp. 295-300, 2011.

M. R. Khayyambashi and F. S. Rizi, “An approach for detecting profile cloning in online social networks,” 2013 7th Intenational Conf. E-Commerce Dev. Ctries. With Focus e-Security, ECDC 2013, pp. 1-12, 2013.

F. S. Rizi and M. R. Khayyambashi, “Profile Cloning in Online Social Networks,” Int. J. Comput. Sci. Inf. Secur., vol. 11, no. 8, pp. 82-86, 2013.

Q. Cao, X. Yang, J. Yu, and C. Palow, “Uncovering Large Groups of Active Malicious Accounts in Online Social Networks,” Proc. 2014 ACM SIGSAC Conf. Comput. Commun. Secur. - CCS ’14, pp. 477-488, 2014.

J. Lescovec, “Stanford University - Data Repository,” 2012. [Online]. Available: https://snap.stanford.edu/data/egonets-Facebook.html. [Accessed: 10-May-2018].

S. Mazhari, S. M. Fakhrahmad, and H. Sadeghbeygi, “A user-profile-based friendship recommendation solution in social networks,” J. Inf. Sci., vol. 41, no. 3, pp. 284-295, 2015.

D. Dave, N. Mishra, and S. Sharma, “Detection Techniques of Clone Attack on Online Social Networks: Survey and Analysis,” pp. 179-186.

Facebook, “Finding Friends and people you may know,” 2018. [Online]. Available: www.facebook.com/help/www/336320879782850. [Accessed: 10-Dec-2018].

V. A and R. I. M. Dunbar, “Evolutionary Dynamics in Twitter Ego Networks,” 2015. [Online]. Available: https://www.sciencedirect.com/topics/computer-science/jaccard-coefficient. [Accessed: 23-Sep-2018].

“Evaluation of clustering - Stanford NLP Group,” 2009. [Online]. Available:https://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-clustering-1.html. [Accessed: 03-Jan-2019].

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).