Impersonating users’ identity in Online Social Networks (OSNs) is one of the open dilemmas from security and privacy point of view. Scammers and adversaries seek to create set of fake profiles to carry out malicious behaviors and online social crimes in social media. Recognizing the identity of Fake Profiles is an urgent issue of concern to the attention of researchers. In this paper, we propose a detection technique called Fake Profile Recognizer (FPR) for verifying the identity of profiles, and detecting the fake profiles in OSNs. The detection method in our proposed technique is based on utilizing Regular Expression (RE) and Deterministic Finite Automaton (DFA) approaches. We evaluated our proposed detection technique on three datasets types of OSNs: Facebook, Google+, and Twitter. The results explored high Precision, Recall, accuracy, and low False Positive Rates (FPR) of detecting Fake Profiles in the three datasets.

Online Social Networks (OSN) are considering one of the most popular internet applications which attract millions of users around the world to build several social relationships. Emerging the Web 2.0 technology allowed OSN users to create, share, or exchange types of contents in a popular fashion. The other hand, OSN are considering one of the most popular platforms for the intruders to spread several types of OSN attacks. Creating fake profiles for launching cloning attacks is one of the most risky attacks which target Users' profiles in Online Social Networks, the attacker seek to impersonate user's identity through duplicating user's online presence in the same or across several social networks, therefore, he can deceive OSN users into forming trusting social relations with his created fake profiles. These malicious profiles aim to harvest sensitive user's information or misuse the reputation of the legitimate profile's owner, as well as it may be used as a spy profiles for other criminal parties. Detecting these fake profiles still represent a major problem from OSN Security and Privacy point of view. In this paper we introduced a theoretical framework which depends on a novel topology of a social graph called Trusted Social Graph (TSG) which used to visualize trusted instances of social communications between OSN users. Another contribution is a proposed detection model that based on TSG topology as well as two techniques; Deterministic Finite Automaton (DFA) and Regular Expression. Our proposed detection model used to recognize the stranger instances of communications and social actions that performed using fake profiles in OSN.