Criminal Act Detection and Identification Model

Abstract

In this paper, we are examining and analyzing human behavior model throughout reality data sources to extract patterns and clues of criminal and suspicious acts. Reality data composed of the digital traces people leave while interacting with computing devices. In this paper, we are focusing on data from people's interaction with social networks and mobile usage such as location markers and call logs. This work also introduces a model for detecting suspicious behavior based on social network feeds. It is based on classification model that can categorize a set of input actions and movements into three types of behavior, criminal, suspicious, or normal. The proposed system is expected to help crime analysts create faster and precise decisions.The model is expected to provide a behavior profile to help crime analysts in the process of crime prevention, understanding crime motivations, and proactive policing.