论文标题
通过时间序列预测，在呼叫详细记录上进行异常检测的混合模型
Hybrid Model for Anomaly Detection on Call Detail Records by Time Series Forecasting
论文作者
论文摘要
移动网络运营商存储了大量信息，例如描述各种事件和用户活动的日志文件。 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.通过异常检测，可以确定一个区域或特定人员中网络流量的异常减少或增加。本文的主要目标是在细胞网络中研究订户的行为，主要预测区域中的呼叫数量并检测网络流量中的异常情况。在本文中，提出了一种基于各种异常检测方法（例如GARCH，K-均值和神经网络）来确定异常数据的新混合方法。此外，我们已经讨论了此类异常的可能原因。
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.
