Detecting cloning attack in Social Networks using classification and clustering techniques

Social Networks (SN) are popular among the people to interact with their friends through the internet. Users spending their time in popular social networking sites like facebook, Myspace and twitter to share the personal information. Cloning attack is one of the insidious attacks in facebook. Usually attackers stole the images and personal information about a person and create the fake profile pages. Once the profile gets cloned they started to send a friend request using the cloned profile. Incase if the real users account gets blocked, they used to send a new friend request to their friends. At the same time cloned one also sending the request to the person. At that time it was hard to identify the real one for users. In the proposed system the clone attack is detected based on user action time period and users click pattern to find the similarity between the cloned profile and real one in facebook. Using Cosine similarity and Jaccard index the performance of the similarity between the users is improved.