Aggregated Traffic Anomaly Detection Using Time Series Forecasting on Call Detail Records

• Published in: Security and communication networks 2022-03, Vol.2022, p.1-9
• Main Authors: Mokhtari, Arian, Ghorbani, Niloofar, Bahrak, Behnam
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Anomalies; Behavior; Cellular communication; Clustering; Communications traffic; Cybersecurity; Datasets; Forecasting; Forecasting techniques; Machine learning; Neural networks; Statistical methods; Stochastic models; Time series; Traffic congestion; Wireless networks
• ISSN: 1939-0114; 1939-0122
• DOI: 10.1155/2022/1182315

Mobile network operators store an enormous amount of information like log files that describe various events and users’ activities. Analysis of these logs might be used in many critical applications such as detecting cyber attacks, finding behavioral patterns of users, security incident response, and network forensics. In a cellular network, call detail records (CDRs) is one type of such logs containing metadata of calls and usually includes valuable information about contacts such as the phone numbers of originating and receiving subscribers, call duration, the area of activity, type of call (SMS or voice call), and a timestamp. With anomaly detection, it is possible to determine abnormal reduction or increment of network traffic in an area or for a particular person. This paper’s primary goal is to study subscribers’ behavior in a cellular network, mainly predicting the number of calls in a region and detecting anomalies in the network traffic. In this paper, a new hybrid method is proposed based on various anomaly detection methods such as GARCH, K-means, and neural network to determine the anomalous data. Moreover, we have discussed the possible causes of such anomalies.