  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, network forensics, etc. In a cellular network Call Detail Records
(CDR) is one type of such logs containing metadata of calls and usually
includes valuable information about contact 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.
