Application of system calls in abnormal user behavioral detection in social networks

Abnormal user detection is one of the key issues in online social network security research. Attackers spread advertising and other malicious messages through stolen accounts, and malicious actions seriously threaten the information security of normal users with the credit system of social networks. For this reason, in the literature, there are a considerable amount of research work which detect abnormal accounts in social networks, however, these efforts ignore the problem of the seamless integration of machine learning with human behaviour-based analysis. This paper reviews the main achievements of abnormal account detection in online social networks in recent years from three aspects: behavioral characteristics, content-based, graph-based, and proposes a new social network abnormal user detection method based on system calls in computer’s kernel. Using enumeration sequence and hidden semi-Markov method, a hierarchical model of anomaly user detection in social networks is established.