Detecting Abnormal Start-Ups, Unusual Resource Consumptions of the Smart Phone: A Deep Learning Approach

doi:10.12142/ZTECOM.201902006

ZTE Communications ›› 2019, Vol. 17 ›› Issue (2): 38-43.DOI: 10.12142/ZTECOM.201902006

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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, 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

Received:2018-01-17 Online:2019-06-11 Published:2019-11-14
About author:ZHENG Xiaoqing (zhengxq@fudan.edu.cn) received the Ph.D. degree in computer science from Zhejiang University, China in 2007. After that, he joined the faculty of School of Computer Science at Fudan University, China. He did research on semantic technology during his stay at the Information Technology Group, Massachusetts Institute of Technology (MIT), USA as an international faculty fellow from 2010 to 2011. His current research interests include natural language processing, deep learning, data analytics and semantic web. He published more than 30 academic papers in various journals and conferences, including ACL, IJCAI, AAAI, WWW, EMNLP, etc|LU Yaping received his M.S. degree in industrial management engineering from Shanghai Jiao Tong University, China in 1988 and that in computer science from the University of Texas at Arlington, USA in 1996. He was the chief engineer of the North America R&D Center of ZTE’s Terminal Business Division from 2015 to 2018. He worked as a senior staff engineer at Motorola Inc from 1997 to 2006, and a solution architect at i2 Technologies from 2006 to 2009 separately before he joined ZTE (USA) in 2010. His research interests include artificial intelligence, operations research, and their applications|PENG Haoyuan received the M.S. degree in software engineering from Fudan University, China in 2015. He has been doing research on machine learning and natural language processing, supervised by Professor ZHENG Xiaoqing|FENG Jiangtao received the M.S. degree in software engineering from Fudan University, China in 2019. He has been doing research on machine learning and natural language processing, supervised by Professor ZHENG Xiaoqing|ZHOU Yi received the B.S. degree in software engineering from Fudan University, China in 2017. He has been doing research on machine learning and natural language processing, supervised by Professor ZHENG Xiaoqing|JIANG Min was the system software engineer with the Software R&D Center of ZTE’s Terminal Business Division. His research areas include Android system, embedded operating system, virtualization technology and machine learning|MA Li received his B.S. degree from the University of Electronic Science and Technology, China in 1993. He has been working in ZTE Corporation since 1999, where he has held important software development, design and management positions in the Network Division, Technology Center and Mobile Device Division. He is currently working as the chief software engineer in the ZTE Terminal Business Division. His research interests and development field include terminal system design, AI engine deployment and optimization, algorithm R&D, and other core terminal technologies|ZHANG Ji received the B.S. degrees in microelectronics and software engineering from Xidian University, China in 2013. He is currently a software engineer with ZTE Corporation. His research areas include Android system, embedded operating system, virtualization technology and machine learning|JI Jie received his master’s engineering degree in computer science software engineering from Xidian University, China in 2008. He is currently a software designer in the Research and Development Team of ZTE Corporation. His research interests include wireless communication technology, AI in embedded systems, and smart phone operating system

Abstract

Abstract:

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.

Key words: deep learning, time series analysis, convolutional neural network, RNN

ZHENG Xiaoqing, LU Yaping, PENG Haoyuan, FENG Jiangtao, ZHOU Yi, JIANG Min, MA Li, ZHANG Ji, JI Jie. Detecting Abnormal Start-Ups, Unusual Resource Consumptions of the Smart Phone: A Deep Learning Approach[J]. ZTE Communications, 2019, 17(2): 38-43.

Figures/Tables 7

References 26

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Event	Argument list
am_proc_start	Package; component; start type
am_proc_died	package

Event	Argument list
am_proc_start	Package; component; start type
am_proc_died	package

Features	Comments
Event	am_proc_start / am_proc_died
Package	which package to start
Component	which component to start
Start type	the startup mode
R5	whether R5 is satisfied

Features	Comments
Event	am_proc_start / am_proc_died
Package	which package to start
Component	which component to start
Start type	the startup mode
R5	whether R5 is satisfied

Hyper-parameter	Value
Size of the LSTM output	2 × 50
Size of the hidden layer in classifier	50
Learning rate	0.001
Batch size	32

Detecting Abnormal Start-Ups, Unusual Resource Consumptions of the Smart Phone: A Deep Learning Approach

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Figures/Tables 7

References 26

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Metrics

Rules	Number of logs labelled by each rule
R1	95β138
R2	269β348
R3	390β203
R4	19β557
R5	82β912
R6	207β881
R4, R5, R6	310β350
Total	1β065β039

Data set	Accuracy (%)
Deep learning model (for the part with future information involved)	98.93
Hybrid model	99.69

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	CPU	Memory
Precision	0.963	0.972
Recall	0.983	0.998
F1-score	0.973	0.985

	CPU	Memory
Precision	0.963	0.972
Recall	0.983	0.998
F1-score	0.973	0.985