Project Title

Secure Traffic Cabinets

Academic department under which the project should be listed

Electrical Engineering

Faculty Sponsor Name

Billy Kihei

Not needed

Project Type

Event

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.

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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.