Dickson polynomial-based secure group authentication scheme for Internet of Things

Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli's Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency.

Evaluation of microalgae and cyanobacteria as potential sources of antimicrobial compounds

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Three microalgal extracts show antimicrobial activity against gram-positive bacteria.

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Different purification techniques were used to reveal the bioactive compounds.

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Linoleic acid, oleic acid, DHA and EPA inhibited the growth of gram-positive bacteria.

In recent decades, marine microorganisms have become known for their ability to produce a wide variety of secondary bioactive metabolites. Several compounds have been isolated from marine microorganisms for the development of novel bioactives for the food and pharmaceutical industries. In this study, a number of microalgae were evaluated for their antimicrobial activity against gram-positive and gram-negative bacteria, including food and plant pathogens, using various extraction techniques and antimicrobial assays. Disc diffusion and spot-on-lawn assays were conducted to confirm the antimicrobial activity. To measure the potency of the extracts, minimum inhibition concentrations (MIultCs) were measured. Three microalgae, namely Isochrysis galbana, Scenedesmus sp. NT8c, and Chlorella sp. FN1, showed strong inhibitory activity preferentially against gram-positive bacteria. These microalgal species were then selected for further purification and analysis, leading to compound identification. By using a mixture of different chromatography techniques gas chromatography–mass spectrometry (GC–MS) and high-performance liquid chromatography (HPLC) and ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS), we were able to separate and identify the dominant compounds that are responsible for the inhibitory activity. Additionally, nuclear magnetic resonance (NMR) was used to confirm the presence of these compounds. The dominant compounds that were identified and purified in the extracts are linoleic acid, oleic acid, docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA). These compounds are the potential candidates that inhibit the growth of gram-positive bacteria. This indicates the potential use of microalgae and their antimicrobial compounds as biocontrol agents against food and plant pathogens.