چکیده (انگلیسی):

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.