论文标题
移动设备的使用分析
Usage Analysis of Mobile Devices
论文作者
论文摘要
移动设备以智能手机，平板电脑和智能手表的形式从仅通信设备发展为不可或缺的一部分。现在，设备比以往任何时候都更加个性化，并且比其他任何人都提供有关人的更多信息。提取用户行为非常困难且耗时，因为以前的大多数工作都是手动或需要提取功能的。在本文中，通过深度学习网络（DNN）提出了一种新颖的用户行为检测方法。最初的方法是将复发性神经网络（RNN）与LSTM一起使用移动设备进行完全无监督的分析。下一种方法是通过使用长期短期内存（LSTM）来理解用户行为来提取功能，然后将其馈入卷积神经网络（CNN）。这项工作主要集中于检测用户行为和用于移动设备的使用分析的异常检测。将这两种方法与某些基线方法进行了比较。实验是在公开可用的数据集上进行的，以表明这些方法可以成功捕获用户行为。
Mobile devices have evolved from just communication devices into an indispensable part of people's lives in form of smartphones, tablets and smart watches. Devices are now more personal than ever and carry more information about a person than any other. Extracting user behaviour is rather difficult and time-consuming as most of the work previously has been manual or requires feature extraction. In this paper, a novel approach of user behavior detection is proposed with Deep Learning Network (DNN). Initial approach was to use recurrent neural network (RNN) along with LSTM for completely unsupervised analysis of mobile devices. Next approach is to extract features by using Long Short Term Memory (LSTM) to understand the user behaviour, which are then fed into the Convolution Neural Network (CNN). This work mainly concentrates on detection of user behaviour and anomaly detection for usage analysis of mobile devices. Both the approaches are compared against some baseline methods. Experiments are conducted on the publicly available dataset to show that these methods can successfully capture the user behaviors.
