Toward Detecting Malicious Activities in Online Social Network through User Behavior
Sofi Nabila · International Journal for Research in Applied Science and Engineering Technology · 2019
Social networks consist of context-sensitive and relational data while also including a considerable amount of malicious content. It has turned out to be troublesome to clarify the genuine semantic estimation of distributed substance for the identification of client practices. Without comprehension the logical foundation, an integrated social media content analysis platform that leverages three levels of features, i.e., user-generated content, social graph connections, and user profile activities, to analyze and detect anomalous behaviors that deviate significantly from the norm in large-scale social networks. Several types of analyses have been conducted for a better understanding of the different user behaviors in the detection of highly adaptive malicious users. In the proposed method, propose a directed graphs, to detect fraudulent users in OSNs.In this, given a training dataset we estimate the posterior probability distribution for each user and uses it to predict a user's label. I.