Fake Profile Identification in Social Network Using Machine Learning and NLP
Keywords:
Machine Learning, Natural Language Processing, Classification.Abstract
At present social network sites are part of the life for most of the people. Every day several people are creating their profiles on the social network platforms and they are interacting with others independent of the user’s location and time. The social network sites not only providing advantages to the users and also provide security issues to the users as well their information. To analyze, who are encouraging threats in social network we need to classify the social networks profiles of the users. From the classification, we can get the genuine profiles and fake profiles on the social networks. Traditionally, we have different classification methods for detecting the fake profiles on the social networks. But, we need to improve the accuracy rate of the fake profile detection in the social networks. In this paper we are proposing Machine learning and Natural language Processing (NLP) techniques to improve the accuracy rate of the fake profiles detection. We can use the Support Vector Machine (SVM) and Naïve Bayes algorithm.
References
- Nazir, Atif, Saqib Raza, Chen-Nee Chuah, Burkhard Schipper, and C. A. Davis. “Ghostbusting Facebook: Detecting and Characterizing Phantom Profiles in Online Social Gaming Applications.” In WOSN. 2010.
- Adikari, Shalinda, and Kaushik Dutta. “Identifying Fake Profiles in LinkedIn.” In PACIS, p. 278. 2014.
- Chu, Zi, Steven Gianvecchio, Haining Wang, and Sushil Jajodia. “Who is tweeting on Twitter: human, bot, or cyborg?.” In Proceedings of the 26th annual computer security applications conference, pp. 21- 30. ACM, 2010.
- Stringhini, Gianluca, Gang Wang, Manuel Egele, Christopher Kruegel, Giovanni Vigna, Haitao Zheng, and Ben Y. Zhao. “Follow the green: growth and dynamics in twitter follower markets.” In Proceedings of the 2013 conference on Internet measurement conference, pp. 163-176. ACM, 2013.
- Thomas, Kurt, Damon McCoy, Chris Grier, Alek Kolcz, and Vern Paxson. “Trafficking Fraudulent Accounts: The Role of the Underground Market in Twitter Spam and Abuse.” In Presented as part of the 22nd {USENIX} Security Symposium ({USENIX} Security 13), pp. 195-210. 2013.
- Farooqi, Gohar Irfan, Emiliano De Cristofaro, Arik Friedman, Guillaume Jourjon, Mohamed Ali Kaafar, M. Zubair Shafiq, and Fareed Zaffar. “Characterizing Seller-Driven Black-Hat Marketplaces.” arXiv preprint arXiv: 1505.01637 (2015).
- Viswanath, Bimal, M. Ahmad Bashir, Mark Crovella, Saikat Guha, Krishna P. Gummadi, Balachander Krishnamurthy, and Alan Mislove. “Towards detecting anomalous user behavior in online social networks.” In 23rd {USENIX} Security Symposium ({USENIX} Security 14), pp. 223-238. 2014.
- O. Dekel, O. Shamir, and L. Xiao. “Learning to classify with missing and corrupted features Machine Learning’’, 81(2):149{178, 2010.
- C.Venkatesan, P. Karthigaikumar, A. Paul, S. Satheeskumaran and R. Kumar, "ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications," in IEEE Access, vol. 6, pp. 9767-9773, 2018. doi: 10.1109/ACCESS.2018.2794346.
- Venkatesan, C., Karthigaikumar, P. & Varatharajan, R. Multimed Tools Appl (2018). “A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection’’1-10. https://doi.org/10.1007/s11042-018-5
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT
This work is licensed under a Creative Commons Attribution 4.0 International License.