Trojan Detection in Digital Microfluidic Biochips via Image Classification: A Deep-Learning Based Approach

The emerging technology of Lab-on-Chips has made a profound impact in the field of healthcare, biochemistry, and molecular biology involving key tasks such as clinical trials, drug therapy, DNA-sequencing, among other tasks. In particular, digital microfluidic biochips have found versatile applications because of the simplicity of operations and low operational cost. Unfortunately, the ease of programmability and controllability in these biochips open doors to severe infringement of privacy and security, which in turn jeopardizes the trustworthiness of bioprotocols. The insertion of Trojans in these biochips may result in deadly outcomes and thus, ensuring the security of biochips has become a major challenge so as to prevent malicious alterations, data theft, cybercrime, sabotage, breach of confidence, and vandalism. Conventional techniques for Trojan detection such as side-channel analysis, runtime scan, and reverse engineering suffer from many shortcomings that compromise time, cost, and the reliability of bioprotocols. In this paper, we present new countermeasures to protect digital microfluidic biochips against Trojan attacks based on image classification of the running bioprotocol. A deep-learning approach is employed to classify the observed snapshot sequences as good or infected. Our results on five real-life bioprotocols demonstrate that the proposed classifier achieves 98.52% accuracy while preserving the security and functionalities of the digital microfluidic biochip.