This paper presents a novel approach for detecting fraudulent activities in mobile telecommunication networks by using a possibilistic fuzzy c-means clustering. Initially, the optimal values of the clustering parameters are estimated experimentally. The behavioral profile modelling of subscribers is then done by applying the clustering algorithm on two relevant call features selected from the subscriber’s historical call records. Any symptoms of intrusive activities are detected by comparing the most recent calling activity with their normal profile. A new calling instance is identified as malicious when its distance measured from the profile cluster centers exceeds a preset threshold. The effectiveness of our system is justified by carrying out large-scale experiments on a real-world dataset.