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.

Microfluidic biochips are a prominent class of lab-on-a-chip, i.e. LoC system, guided with some activation sequences devised for a bioprotocol to manipulate fluids used in biomedical and biochemical laboratory syntheses. This paper is a survey of reliability issues for state-of-the-art microfluidic biochips. First, this paper presents the basic structure and working principles for different types of available microfluidic biochips. After that, it elaborates on a systematic analysis of security and reliability issues in performing different fluidic operations using microfluidic biochips. This paper also presents the current attack models and types with their scenarios in different remedy actions, both proactive and reactive. Finally, this paper concludes with future research directions so that the effects due to attacks on biochips will be minimized within the stipulated time for a reliable and sustainable microfluidic biochip world.

In this paper, an approach of multi-view learning, with multilayer perceptron (MLP) and radial basis functions (RBF) with dynamic decay adjustment (DDA), has been proposed. Three different categories of semi-supervised learning are multi-view training, co-training and self-training. Here we have only used self-training and multi-view learning mechanisms to train the classifier. To test the accuracy of the algorithms, we have taken five real-time datasets from UCI Machine Learning Repository. The classifier is trained using the perceptron learning rule with its supervised and semi-supervised (self-training) versions and MLP with RBF (multi-view learning). The average classification accuracies have been compared and the proposed algorithm outperforms the former versions on the specified training sets. The significant improvement in performance obtained using multi-view learning can be used for various fields such as detecting changes of images, speech recognition and biometric identification.