Fake Accounts and Clone Profiles Identification on Social Media Using Machine Learning Algorithms
DOI:
https://doi.org/10.32628/IJSRSET2293158Keywords:
Machine Learning, Fake and Clone Profiles, Twitter, Social Media, Classification Algorithms.Abstract
Fake and Clone profiles are creating dangerous security problems to social network users. Cloning of user profiles is one serious threat, where already existing user’s details are stolen to create duplicate profiles and then it is misused for damaging the identity of original profile owner. They can even launch threats like phishing, stalking, spamming etc. Fake profile is the creation of profile in the name of a person or a company which does not really exist in social media, to carry out malicious activities. In this paper, a detection method has been proposed which can detect Fake and Clone profiles in Twitter. Fake profiles are detected based on number of abuse reports, number of comments per day and number of rejected friend requests, a person who are using fake account. For Profile Cloning detection two Machine Learning algorithms are used. One using Random forest Classification algorithm for classifying the data and Support Vector Machine algorithm. This project has worked with other ML algorithms, those training and testing results are included in this paper.
References
- Dr.Narsimha.G, Dr.JayadevGyani, P. Srinivas Rao ,"Fake Profiles Identification in Online Social Networks Using Machine Learning and NLP(2018)", International Journal of Applied Engineering Research ISSN 0973-4562, Number 6, Volume 13.
- Adikari, S., Dutta, K., 2014. Identifying Fake Profiles in Linkedin, in: PACIS 2014 Proceedings. Presented at the Pacific Asia Conference on Information Systems.
- Stringhini, G., Kruegel, C., Vigna, G., 2010. Detecting Spammers on Social Networks, in: Proceedings of the 26th Annual Computer Security Applications Conference, ACSAC ’10. ACM, New York, NY, USA, pp. 1–9. doi:10.1145/1920261.1920263.
- Aleksei Romanov, Alexander Semenov, Oleksiy Mazhelis and Jari Veijalainen, Detection of Fake Profiles in Social Media Literature Review, University of Jyväskylä, Finland.
- Michael Fire et al. (2012). "Strangers intrusion detection-detecting spammers and fake profiles in social networks based on topology anomalies." Human Journal 1(1): 26-39.Günther, F. and S. Fritsch (2010).
- Stein T, Chen E, Mangla K,” Facebook immune system. In: Proceedings of the 4th workshop on social network systems”, ACM 2011, pp.1-8.
- Saeed Abu-Nimeh, T. M. Chen, and O. Alzubi, “Malicious and Spam Posts in Online Social Networks,” Computer, vol.44, no.9, IEEE2011, pp.23–28.
- Maclean, J. Melton, J. Melton, A. R. Simon, and M. Chisholm, Data Mining : Concepts and Techniques. 1999.
- S. Kiruthiga, “Detecting Cloning Attack in Social Networks Using Classification and Clustering Techniques,” IEEE, 2014.
- Harini .N., Smruthi.M.,"A Hybrid Scheme for Detecting FakeAccounts in Facebook" ISSN: 2277- 3878, (IJRTE)International Journal of Recent Technology and Engineering (2019) , Issue-5S3, Volume-7.
- https://www.statisticssolutions.com/correlation-pearson-kendall-spearman
- https://www.cs.princeton.edu/~schapire/talks/picasso-minicourse.pdf
Downloads
Published
Issue
Section
License
Copyright (c) IJSRSET
This work is licensed under a Creative Commons Attribution 4.0 International License.