Project Title

Secure Traffic Cabinets

Academic department under which the project should be listed

SPCEET - Electrical and Computer Engineering

Research Mentor Name

Billy Kihei

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.

Disciplines

Digital Communications and Networking | Hardware Systems | Signal Processing

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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.