Online Social Networks (OSNs) allow users to create a public or private profile, encourage sharing information and interests with other users and communicating with each other. Though online social network increase gradually there are still lot of security and privacy concerns. In such a scenario, it would be very beneficial to have a mechanism able to assign a risk score to each OSN user. Unfortunately, very often users are not aware of this exposure as well as the serious consequences this might have. Also, some users are less concerned about information privacy; therefore, they post more sensitive information on their profiles without specifying appropriate privacy settings and this can lead to security risks. In such case it will be useful for creating the risk score to identify the anomalous behavior of user from normal behavior. Hence the system propose a risk assessment based on the idea that the more a user behavior diverges from what it can be considered as a ‘normal behavior’, the more it should be considered risky. In doing this, the system take in into account that OSN population is really heterogeneous in observed behaviors. As such, it is not possible to define a unique standard behavioral model that fits all OSN users’ behaviors. However, it expect that similar people tend to follow the similar rules with the results of similar behavioral models. The carried out experiments on a real Facebook dataset show that the proposed model outperforms a simplified behavioral-based risk assessment.