Detecting Anomalies in Mobile Telecommunication Networks Using a Graph Based Approach

According to a survey conducted by the Communications Fraud Control Association an estimated $46.3 billion were lost due to telecommunications fraud in 2013. This suggests that the potential for intentional exploitation of unsuspecting users is an ongoing issue, and finding anomalies in telecommunications data can aide in the security of users, their phones, their personal information, and the companies that provide them services. Most anomaly detection approaches applied to this type of data use some type of statistical representation; however, we think that a more natural representation is to consider telecom traffic as a graph. In this paper, we specifically focus on using graphbased anomaly detection to find and report anomalies in telecom data. Up until now, little work seems to be focused on detecting and reporting anomalies in telecommunications data represented as a graph. Moreover, even less work seems to focus on detecting anomalies in phone call history with this same representation. Our goal in this application paper is to use real-world cell phone traffic to detect anomalies in user patterns based on phone call and text message history.