Open Access
Subscription Access
Fake Profile Detection in Instagram Online Social Network
Social network provides number of applications such as Myspace, Facbook, Twitter and many more through which users can connect with their friends and share their images and videos with them. Instagram is application of social network which is used to share images and videos with friends also tag a friend on an image and video. It is difficult to recognize which user is normal user or which user is malicious user. In this paper different techniques to recognize fake profile user has been surveyed and provide a mechanism to detect fake profiles in social network. This paper proposed a mechanism to detect normal posts using Random Forest classifier. The proposed mechanism has been analyzed using weka.
Keywords
Social Networks, Fake Profile, Cloning ,Instagram and Facebook.
User
Font Size
Information
- Pran Dev, Jyoti, Dr. Kulvinder Singh and Dr. Sanjeev Dhawan, “A Naive Algorithmic Approach for Detection of Users’ with Unusual Behavior in online Social Networks” International Journal of Software and Web Sciences (IJSWS), ISSN: 2279-0071pp: 37-41,2015.
- Ekta and Sanjeev Dhawan, “Classification of Data Mining and Analysis for Predicting Diabetes Subtypes using WEKA”, Vivechana: National Conference on Advances in Computer Science and Engineering (ACSE-2016), pp. 1-5.
- Ekta, Sanjeev Dhawan and Kulvinder Singh, “Feature Extraction and Content Investigation of Facebook User’s using Netvizz and Gephi”, Advances in Computer Science and Information Technology (ACSIT), ACSIT 2016, pp. 262-265.
- Sanjeev Dhawan and Ekta, “Implications of Various Fake Profile Detection Techniques in Social Networks”, IOSR Journal of Computer Engineering (IOSR-JCE), AETM'16, 2016, pp. 49-55.
- M. Li, N. Cao, S. Yu, and W. Lou, “FindU: Privacy-preserving personal profile matching in mobile social networks,” in Proc. IEEE INFOCOM, Shanghai, China, 2011, pp. 2435–2443.
- W. Dong, V. Dave, L. Qiu, and Y. Zhang, “Secure friend discovery in mobile social networks,” in Proc. IEEE INFOCOM, Shanghai, China, 2011, pp. 1647–1655.
- R. Agrawal and R. Srikant, “Privacy-preserving data mining,” in ACM Sigmod Rec., vol. 29, no. 2, pp. 439–450, 2000.
- A. Narayanan, N. Thiagarajan, M. Lakhani, M. Hamburg, and D. Boneh, “Location privacy via private proximity testing,” in Proc. NDSS, San Diego, CA, USA, 2011.
- Sazzadur Rahman, Ting-Kai Huang, Harsha V. Madhyastha, and Michalis Faloutsos, “Detecting Malicious Facebook Applications”, IEEE/ACM TRANSACTIONS ON NETWORKING, IEEE 2015, pp. 1-15
- G. Stringhini, C. Kruegel, and G. Vigna, “Detecting spammers on social networks,” in ACSAC ’10: Proceedings of the 26th Annual Computer Security Applications Conference. ACM Request Permissions, 2012, pp. 1–9.
- Anwar M, Fong PW, “A visualization tool for evaluating access control policies in Facebook-style social network systems”, In: Proceedings of the 27th annual ACM symposium on applied computing, ACM 2012, pp. 1443–1450.
- S. Abu-Nimeh, T. M. Chen, and O. Alzubi, “Malicious and Spam Posts in Online Social Networks,” Computer, vol. 44, no. 9, IEEE 2011, pp. 23–28.
- Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon, “What is Twitter, a Social Network or a News Media?”, International World Wide Web Conference Committee (IW3C2),ACM 2010, pp. 1-10.
- Rahman MS, Huang TK, Madhyastha HV, Faloutsos M, “Frappe: detecting malicious Facebook applications”, in: Proceedings of the 8th international conference on emerging networking experiments and technologies, ACM 2012, pp. 313–324.
Abstract Views: 206
PDF Views: 0