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首页> 外文期刊>IEEE transactions on industrial informatics >Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network
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Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network

机译：大数据分析，用于移动无线网络中的用户活动分析和用户异常检测

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摘要

The next generation wireless networks are expected to operate in fully automated fashion to meet the burgeoning capacity demand and to serve users with superior quality of experience. Mobile wireless networks can leverage spatio-temporal information about user and network condition to embed the system with end-to-end visibility and intelligence. Big data analytics has emerged as a promising approach to unearth meaningful insights and to build artificially intelligent models with assistance of machine learning tools. Utilizing aforementioned tools and techniques, this paper contributes in two ways. First, we utilize mobile network data (Big Data)—call detail record—to analyze anomalous behavior of mobile wireless network. For anomaly detection purposes, we use unsupervised clustering techniques namely k-means clustering and hierarchical clustering. We compare the detected anomalies with ground truth information to verify their correctness. From the comparative analysis, we observe that when the network experiences abruptly high (unusual) traffic demand at any location and time, it identifies that as anomaly. This helps in identifying regions of interest in the network for special action such as resource allocation, fault avoidance solution, etc. Second, we train a neural-network-based prediction model with anomalous and anomaly-free data to highlight the effect of anomalies in data while training/building intelligent models. In this phase, we transform our anomalous data to anomaly-free and we observe that the error in prediction, while training the model with anomaly-free data has largely decreased as compared to the case when the model was trained with anomalous data.
机译：下一代无线网络有望以全自动方式运行，以满足不断增长的容量需求，并为用户提供卓越的体验质量。移动无线网络可以利用有关用户和网络状况的时空信息，将系统嵌入端到端可见性和智能。大数据分析已成为一种发掘有意义的见解并借助机器学习工具构建人工智能模型的有前途的方法。利用上述工具和技术，本文通过两种方式做出贡献。首先，我们利用移动网络数据（大数据）（呼叫详细记录）来分析移动无线网络的异常行为。为了进行异常检测，我们使用了无监督聚类技术，即k均值聚类和分层聚类。我们将检测到的异常与地面真实信息进行比较，以验证其正确性。从比较分析中，我们观察到，当网络在任何位置和任何时间突然遇到高（异常）流量需求时，它将其识别为异常。这有助于识别网络中感兴趣的区域以采取特殊措施，例如资源分配，故障避免解决方案等。其次，我们训练基于神经网络的预测模型，该模型具有异常和无异常数据，以突出显示异常的影响。训练/建立智能模型时获取数据。在此阶段，我们将异常数据转换为无异常，并且与使用异常数据训练模型相比，在使用无异常数据训练模型的同时，我们观察到预测误差。

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