Abstract Among all the modern technological advances, digital microfluidic biochip has been extending a salient solution to healthcare and bio-laboratories with the pledge of high sensitivity and reconfigurability. Such biochip devices fulfill the requirement of a faster testing kit for the detection of different novel diseases, which is indispensable in the market due to the tremendously disrupted scenario of healthcare systems. To eliminate erroneous testing, the current scope of digital microfluidic biochips is widened as a viable testing method using various bioprotocols with a reduced cost in developing countries. This paper addresses the existing security challenges and operational faults in the identification mechanism of proteins such as severe acute respiratory syndrome coronavirus 2 spike protein in state-of-the-art digital microfluidic biochips. We are the first to propose a safety detection solution along with a fault identification algorithm using an inductive transfer learning model. Experimental results of the proposed model register a threshold accuracy of 98% while applying the own dataset. This work will provide a better security-enabled fault-free safety assurance framework against attack and fault identification with better accuracy in digital microfluidic biochips for the detection of different diseases and many other healthcare diagnoses, without any overhead of completion time for bioprotocols.

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.