IJMTES – Paper Presented in: ‘2 day State Level workshop on Cyber Fest 17’, conducted by: ‘Department of Computer Engineering, Marathwada Mitra Mandal College of Engineering, Pune’ on 22-23 Feb 2017

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : DETECTION OF FAKE ACCOUNTS IN ONLINE SOCIAL NETWORKS (OSN)

Author’s Name : Ms Gouri N Shinde | Ms Akshada S Shetty | Ms Srushti Chinnapurkar | Ms Mohini Salunkhe | Prof Shailaja Jadhavunnamed

Paper Presented in : ‘2 day State Level workshop on Cyber Fest 17’, conducted by: ‘Department of Computer Engineering, Marathwada Mitra Mandal College of Engineering, Pune’ on 22-23 Feb 2017

Volume 04 Issue 05 2017

ISSN no:  2348-3121

Page no: 1-3

Abstract – In the today’s generation, the social life of everyone has become associated with the online social networks. These sites have made a drastic change in the way we pursue our social life. It has become so much easier to make friends and keeping in contact with them and their updates. But with their rapid growth, many problems like fake profiles online impersonation have also grown. Fake accounts are a preferred means for malicious users of online social networks to send spam, commit fraud, or otherwise abuse the system. A single malicious actor may create dozens to thousands of fake accounts in order to scale their operation. The last few years have witnessed the emergence and evolution of a vibrant research stream on a large variety of online Social Network (OSN) platforms. Recognizing anonymous, yet identical users among multiple OSNs is still an intractable problem. Clearly, cross-platform exploration may help to solve many problems in social computing in both theory and real time .Since public profiles can be duplicated and easily impersonated by users with different purposes, most current user identification resolutions, which mainly focus on text mining of users’ public profiles, are fragile. Since identical users tend to set up partial similarriendship structures in different OSNs, we proposed the Friend Relationship Based User Identification (FRUI) algorithm. FRUI calculates a match possibility for all candidate User Matched Pairs (UMPs), and using UMPs with top ranks are considered as identical users. We also developed propositions to improve the efficiency of the algorithm. Results of extensive experiments demonstrate that FRUI performs much better.

Keywords— Online Social Media, Cross Platform, Anonymous Identical Fake User, Friend Relationship, Database Management

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