With the growing popularity of social networks, identifying Profile Cloning Attacks (PCAs) is an important challenge within the scope of privacy in the communications world. Until now, researchers have identified these attacks by using features such as profile informati
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With the growing popularity of social networks, identifying Profile Cloning Attacks (PCAs) is an important challenge within the scope of privacy in the communications world. Until now, researchers have identified these attacks by using features such as profile information, link information, and interactions information that are based on methods like similarities and network structure. Previously suggested approaches lack specific routine and logic to track an attacker, and begin identifying PCAs with victim direct requests or according to the time of a friend request from an attacker. This research offers a new approach with a total of two major steps. Step one emphasizes that legitimate users are attracted to interactions within their local communities; conversely, attackers are attracted to more dense areas. Step two was designed according to the analysis of the interactive behavior that is obtained from users 'earlier research. With this approach, according to a logic based on network structure, search cloned profiles can be identified. Finally, a list of suspicious nodes to cloned nodes has been introduced with their scores that show the accuracy of selection. During the research, a logical relation between the average degrees of social network graph and the selection of the appropriate suspicious nodes with high priority was extracted. Finally, a general framework is proposed.
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