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.

Correcting grammatical errors in various language contexts is a crucial and challenging task in the field of natural language processing, commonly referred to as Multilingual Grammatical Error Correction. This paper elaborates the Adversarial Temporal Graph Convolution Model (AT-GCM), which combines the capabilities of MT-5, adversarial learning, and temporal graph convolutional neural network (t-GCN) to achieve accurate progress in multilingual grammatical error correction. The inherent capability of MT-5 to process multiple languages simultaneously serves as a powerful embedding generator for the purpose of multilingual error correction. The t-GCN is employed for the purpose of navigating the temporal context and interdependencies present within words. The assumption that modeling the dynamic interactions among words within the context of temporal relationships improves precision, particularly in languages with complex sentence structures, is supported by research. The utilization of adversarial learning techniques can enhance the generalization capabilities of the model across various language pairings, effectively addressing the challenges associated with low-resource languages. A comprehensive analysis is carried out on a diverse, multilingual dataset comprising various languages, viz. English, Russian, German, Czech, Arabic, and Romanian. The experimental results present significant improvements in grammatical error correction performance compared to state-of-the-art models. Our approach effectively resolves grammatical errors in various linguistic contexts by utilizing a combination of MT-5, adversarial learning, and t-GCN.

Among recent technological advances, microfluidic biochips have been leading a prominent solution for healthcare and miniaturized bio-laboratories with the assurance of high sensitivity and reconfigurability. On increasing more unreliable communication networks day-by-day, technological shifts in the fields of communication and security are now converging. In today's cyber threat landscape, these microfluidic biochips are ripe targets of powerful cyber-attacks from different hackers or cyber-criminals. Hence, securing such systems is of paramount importance. This paper presents the security aspects of digital microfluidic biochip layout to protect the confidentiality of layout data from unscrupulous people and man-in-the-middle attacks. We propose an authentication mechanism with an error control mechanism that provides reliability, authentication, trustworthy and safety for both storage and communication of GDS, i.e., Graphical Design System, file generally used for digital microfluidic biochip layouts. Simulation results articulate the efficacy of the proposed security model without the overhead of the bioprotocol completion time. The proposed scheme, which used AES as an encryption algorithm with a 256-bit encryption key, has also shown a speedup of 6.0 (with 85% efficiency) faster than the prior efficient scheme. We hope to develop a secure layout design flow for biochips to achieve better resistance to any attack.

Internet of Things (IoT) has lots of attention in the last decade. The connected IoT devices are more than the total world population. Due to its low cost, easy to deploy, and simple to implement, application areas are large like smart city, smart home, smart transportation, environment monitoring, agriculture and many more. There exists some security and privacy challenges in IoT system. The device identification is one of the challenges in any IoT application. Authentication is one of the processes to identify the device. Though some work has been done on this problem, most of these are using a centralized system. In this paper, we have proposed a distributed authentication system using the Blockchain technology The implementation of the proposed authentication is done on Ethereum platform for its better results in order to justify it as a superior scheme.

Blockchain is the backbone technology behind crypto-currency and Bitcoin. By concept, Blockchain is a distributed database where transactions are recorded in an incorruptible and non-modifiable manner. Currently, Blockchain technology is envisioned as a powerful framework for open-access networks, decentralized information processing and sharing systems, etc. This review is motivated due to the lack of an extensive survey on the existing decentralized consensus mechanisms in Blockchain technology. So in this paper, an in-depth review of the distributed consensus mechanisms has been presented. In addition to this, a comparative analysis of the consensus protocols based on the type of Blockchain is also demonstrated.