• Sowmya P

Online Social Network (OSN) is a network of users with similar areas of interests. With the gaining popularity of OSN day by day, privacy and authentication processes related to it are compromised by attackers. Users of social networks are experiencing extremely risky security breaches. One of the most dangerous cyberthreats is profile cloning, in which data from already-existing users is taken to generate duplicate profiles that are then used to compromise the identity of real users. They then cause other attacks like phishing, stalking, spamming etc. using these clone profiles. So, detection of these types of duplicate profiles in online social sites is essential to avoid adverse effects caused by them. This paper proposes a detection algorithm that can identify Facebook clone profiles. Two techniques are applied to the detection of profile cloning, one utilizing similarity measures and the other employing the C4.5 decision tree technique. Two sorts of similarities are considered in similarity measures: similarity of attributes and similarity of network interactions. By creating a decision tree and taking information gathered into account, C4.5 finds clones. Finally, both the methods are compared to examine how well these two approaches recognize clone profiles.

Clone, Identity Theft, C4.5, Attribute Similarity, Network Similarity

"Detection of Clone Profiles in Facebook using Similarity Measures and Classification Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.g761-g767, April-2024, Available :http://www.jetir.org/papers/JETIR2404697.pdf

"Detection of Clone Profiles in Facebook using Similarity Measures and Classification Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppg761-g767, April-2024, Available at : http://www.jetir.org/papers/JETIR2404697.pdf