• Anusha K G
• Manasa J
• Mahalakshmi
• Siri S
• Chandru A S

Abstract : Online social networks (OSNs) are becoming increasingly popular and Identity Clone Attacks (ICAs) that aim at creating fake identities for malicious purposes on OSNs are becoming a significantly growing concern. Such colluding attacks affect the reliable relationships. The purpose of this paper is to overcome such attacks by matching the user profiles across many OSNs by analyzing and characterizing the behaviors of ICAs. Then, we propose a prediction framework that focuses on identifying suspicious identities. Towards predicting suspicious identities, we propose two approaches for attribute similarities and User activities. The first approach deals with building a classifier based on semi supervised techniques for attribute extraction and matching; and the second one focuses on tracking the user activities. Experimental results are demonstrated to validate the accuracy of our prediction approach.

OSN, ICA, Semi supervised techniques

"Predicting Identity Clone Attacks in Online Social Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.170-173, April-2019, Available :http://www.jetir.org/papers/JETIR1904Q32.pdf

"Predicting Identity Clone Attacks in Online Social Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp170-173, April-2019, Available at : http://www.jetir.org/papers/JETIR1904Q32.pdf