Bot or human? A behavior-based online bot detection system

Abstract

The abuse of Internet online services by automated programs, known as bots, poses a serious threat to Internet users. Bots target popular Internet online services, such as web blogs and online social networks, to distribute spam and malware. In this work, we will first characterize the human behaviors and bot behaviors in online services. Based on the behavior characterization, we propose an effective detection system to accurately distinguish bots from humans. Our proposed detection system consists of two main components: (1) a client-side logger and (2) a server-side classifier. The client-side logger records user behavioral events such as mouse movement and keystroke data, and provides this data in batches to a server-side classifier which identifies a user as human or bot. Our experimental results demonstrate that our proposed detection is able to achieve very high accuracy with negligible overhead.