Design of an Autonomous Intrusion Classification Device for FIDS Robustness

Fence intrusion detection system (FIDS) must ideally detect all malicious intrusions without producing false alarms arising from environmental sources. Classification of intrusions can provide security personnel with additional information regarding the level of threat. Modern FIDS are equipped with several sensing channels capable of recording fence vibrations either optically or mechanically. Introducing autonomy to each channel can improve the robustness of the FIDS as well as introduce resilience to changing conditions such as visibility/weather conditions. In this work, an autonomous accelerometer-based FIDS edge device capable of detecting and classifying intrusions is presented. The FIDS consists of three stages. First, threshold detection is used to flag potential intrusions allowing the microcontroller (MCU) to save power during idle state of fence. In the second stage, the threshold exceedence probability is evaluated to discriminate between background noise and human intrusions. An oscillator model was fitted to derive the parameters of the first two stages based on physical properties of the fence. Third, a convolutional neural network (CNN) was trained to classify the detected disturbances into two types namely rattling and climbing. The intrusion detection stages generated only a single false alarm from 17 hours of storm data while the classification stage produced a 5-fold cross validation accuracy of
$\approx 90.5$
%. The intrusion detection and classification was implemented by rounding weights and using a custom CNN inference engine on an 8-bit MCU. The implementation showed no degradation in classification accuracy and no drift in sampling frequency during real-time operation.