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

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, an...

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Bibliographic Details
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
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Summary: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.
ISSN:1939-0114
1939-0122
DOI:10.1155/2022/1182315