DETECTING ABNORMAL BEHAVIOR IN SOCIAL NETWORK WEBSITES BY USING A PROCESS MINING TECHNIQUE

Abstract

Detecting abnormal user activity in social network websites could prevent from cyber-crime occurrence. The previous research focused on data mining while this research is based on user behavior process. In this study, the first step is defining a normal user behavioral pattern and the second step is detecting abnormal behavior. These two steps are applied on a case study that includes real and syntactic data sets to obtain more tangible results. The chosen technique used to define the pattern is process mining, which is an affordable, complete and noise-free event log. The proposed model discovers a normal behavior by genetic process mining technique and abnormal activities are detected by the fitness function, which is based on Petri Net rules. Although applying genetic mining is time consuming process, it can overcome the risks of noisy data and produces a comprehensive normal model in Petri net representation form.