Abstract
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.
Highlights
Call detail records (CDR) is one of these measurements that is widely employed to discover the behavioral patterns of subscribers in a network [1]
Our contributions towards anomaly detection in the telecommunication domain are as follows: (i) We try to detect the unusual behavior of the users using a hybrid model that utilizes the benefits of three methods: generalized autoregressive conditional heteroscedasticity (GARCH), K-means, and neural networks (ii) We use logistic regression for causality inference (iii) We compare the results of the hybrid model with the previous works
GARCH, neural network, K-means, and logistic regression techniques are used on mobile network data. is type of information is well studied in the literature in terms of anomaly detection. e novelty of this paper is in using the prediction algorithm in a hybridized way
Summary
Many continuously changing network parameters and measurements are obtained from subscribers. Mobile operators use these measurements and other information to improve the performance of their network. Call detail records (CDR) is one of these measurements that is widely employed to discover the behavioral patterns of subscribers in a network [1]. Many of the anomaly detection methods are based on forecasting techniques [3]. Short and medium-term forecasting problems are usually based on identification, modeling, and extrapolation of patterns found in previous data. Due to the lack of significant changes in these earlier data, statistical methods are useful for shortterm and mid-term forecasting
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