A Home Intrusion Detection System using Recycled Edge Devices and Machine Learning Algorithm

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%.

@article{Kwon2020,
  title     = {A Home Intrusion Detection System using Recycled Edge Devices and Machine Learning Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {8},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Daewoo Kwon and Jinseok Song and Chanho Choi and Eun-Kyu Lee},
  doi       = {10.14569/IJACSA.2020.0110804},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110804}
}