Usage Analysis of Mobile Devices

TL;DR

This paper introduces a deep learning-based method for analyzing user behavior and detecting anomalies in mobile device usage, utilizing LSTM and CNN architectures to improve upon baseline methods.

Contribution

It proposes a novel deep learning approach combining LSTM and CNN for unsupervised user behavior detection and anomaly detection on mobile devices.

Findings

Deep learning models effectively capture user behaviors.

LSTM-CNN approach outperforms baseline methods.

Successful detection of anomalies in mobile usage data.

Abstract

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.