K-Means Algorithm: Fraud Detection Based on Signaling Data

Abstract

At present, the crime of telecom fraud, with advanced communications and Internet technologies, is growing rapidly and causing huge losses every year. The traditional fraud detection methods are less flexible. In this paper, we used the signaling data to train a clustering model, which can discover the hidden user characteristics of fraud phones. The paper puts forward the extraction method of behavior characteristics, reduce the dimension of features with principal component analysis and select the appropriate clustering parameters through grid search, then present the K-Means-based behavior identification system, which can help to distinguish the frauds and identify the fraud phone numbers. Finally, the feasibility of this model is verified by the actual sample dataset.