文章基本信息

标题：A Home Intrusion Detection System using Recycled Edge Devices and Machine Learning Algorithm
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作者：Daewoo Kwon ; Jinseok Song ; Chanho Choi 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：8
DOI：10.14569/IJACSA.2020.0110804
出版社：Science and Information Society (SAI)
摘要：This paper proposes a home intrusion detection system that makes the best use of a retired smartphone and an existing Wi-Fi access point. On-board sensors in the smartphone mounted on an entrance door records signals upon unwanted door opening. The access point is reconfigured to serve as a home server and thus it can process sensor data to detect unauthorized access to home by an intruder. Recycling devices enables a home owner to build own security system with no cost as well as helps our society deal with millions of retired devices and waste of computing resources in already-deployed IT devices. In order to improve detection accuracy, this paper proposes a detection method that employs a machine learning algorithm and an analysis technique of time series data. To minimize energy consumption on a battery-powered smartphone, the proposed system utilizes as few sensors as possible and offloads all the computation to the home edge server. We develop a prototype and run experiments to evaluate accuracy performance of the proposed system. Results show that it can detect intrusion with probability of 95% to 100%.
关键词：Security; intrusion detection; edge computing; Internet of Things; recycling