Non-invasive embedded system hardware/firmware anomaly detection based on the electric current signature

Abstract

Quality control is a critical aspect of the modern electronic circuit industry. In addition to being a pre-requisite to proper functioning, circuit quality is closely related to safety, security, and economic issues. Quality control has been reached through system testing. Meanwhile, device miniaturization and multilayer Printed Circuit Boards have increased the electronic circuit test complexity considerably. Hence, traditional test processes based on manual inspections have become outdated and inefficient. More recently, the concept of Advanced Manufacturing or Industry 4.0 has enabled the manufacturing of customized products, tailored to the changing customers’ demands. This scenario points out additional requirements for electronic system testing: it demands a high degree of flexibility in production processes, short design and manufacturing cycles, and cost control. Thus, there is a demand for circuit testing systems that present effectiveness and accessibility without placing numerous test points. This work is focused on automated test solutions based on machine learning, which are becoming popular with advances in computational tools. We present a new testing approach that uses autoencoders to detect firmware or hardware anomalies based on the electric current signature. We built a test set-up using an embedded system development board to evaluate the proposed approach. We implemented six firmware versions that can run independently on the test board – one of them is considered anomaly-free. In order to obtain a reference frame to our results, two other classification techniques (a computer vision algorithm and a random forest classification model) were employed to detect anomalies on the same development board. The outcomes of the experiments demonstrated that the proposed test method is highly effective. For several test scenarios, the correct detection rate was above 99%. Test results showed that autoencoder and random forest approaches are effective. However, random forests require all data classes to be trained. Training an autoencoder, on the other hand, only requires the reference (anomaly-free) class.