Detecting Abnormal Start-Ups, Unusual Resource Consumptions of the Smart Phone: A Deep Learning Approach
ZHENG Xiaoqing1, LU Yaping2, PENG Haoyuan1, FENG Jiangtao1, ZHOU Yi1, JIANG Min2, MA Li2, ZHANG Ji2, and JI Jie2
( 1. School of Computer Science, Fudan University, Shanghai 201203, China;
2. Software R&D Center/Terminal Business Division, ZTE Corporation, Shanghai 201203, China
The temporal distance between events conveys information essential for many time series tasks such as speech recognition and rhythm detection. While traditional models such as hidden Markov models (HMMs) and discrete symbolic grammars tend to discard such information, recurrent neural networks (RNNs) can in principle learn to make use of it. As an advanced variant of RNNs, long short-term memory (LSTM) has an alternative (arguably better) mechanism for bridging long time lags. We propose a couple of deep neural network-based models to detect abnormal start-ups, unusual CPU and memory consumptions of the application processes running on smart phones. Experiment results showed that the proposed neural networks achieve remarkable performance at some reasonable computational cost. The speed advantage of neural networks makes them even more competitive for the applications requiring real-time response, offering the proposed models the potential for practical systems.
deep learning; time series analysis; convolutional neural network; RNN