Secure Traffic Cabinets
Disciplines
Digital Communications and Networking | Hardware Systems | Signal Processing
Abstract (300 words maximum)
Traffic systems are becoming more and more connected and intelligent. As electronics in traffic cabinets become more connected, it is important to secure the electronics in a traffic controller. We develop a machine learning method for detecting lock picking on a traffic cabinet using accelerometer and gyroscope data. We implement our method on an embedded computing platform, the M5StickC Plus. We deploy our embedded system inside traffic cabinets at an offsite location for testing and validation. Currently, our accuracy is above 90% with a low false alarm rate.
Academic department under which the project should be listed
SPCEET - Electrical and Computer Engineering
Primary Investigator (PI) Name
Billy Kihei
Secure Traffic Cabinets
Traffic systems are becoming more and more connected and intelligent. As electronics in traffic cabinets become more connected, it is important to secure the electronics in a traffic controller. We develop a machine learning method for detecting lock picking on a traffic cabinet using accelerometer and gyroscope data. We implement our method on an embedded computing platform, the M5StickC Plus. We deploy our embedded system inside traffic cabinets at an offsite location for testing and validation. Currently, our accuracy is above 90% with a low false alarm rate.