Anomaly Detection in Social-Economic Computing

Abstract

Anomaly detection has been intensively studied in a variety of research fields, including system and network intrusion detections, fraud detections, etc. Current anomaly detection techniques vastly focus on the detection of the anomalous data. This type of approach could be efficient for the sake of system and network intrusion detection. However, for the social related fraud detection, it is not thorough enough for only applying such approach. One reason is that the ignored social or economic environment can directly affect consumers, who can also be the impersonators. Thereby, in this paper, based on the assumption in microeconomics that every single individual can be treated as a consumer, we propose a novel anomaly detection model via social-economic computing. To the best of our knowledge, this is the pioneer research for anomaly detection in social-economic computing.