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Combustion experiments and chemical kinetics simulations generate huge data that is computationally and data intensive. A cloud-based cyberinfrastructure known as CloudFlame is implemented to improve the computational efficiency, scalability and availability of data for combustion research. The architecture consists of an application layer, a communication layer and distributed cloud servers running in a mix environment of Windows, Macintosh and Linux systems. The application layer runs software such as CHEMKIN modeling application. The communication layer provides secure transfer/archive of kinetic, thermodynamic, transport and gas surface data using private/public keys between clients and cloud servers. A robust XML schema based on the Process Informatics Model (PrIMe) combined with a workflow methodology for digitizing; verifying and uploading data from scientific graphs/tables to PrIMe is implemented for chemical molecular structures of compounds. The outcome of using this system by combustion researchers at King Abdullah University of Science and Technology (KAUST) Clean Combustion Research Center and its collaborating partners indicated a significant improvement in efficiency in terms of speed of chemical kinetics and accuracy in searching for the right chemical kinetic data.

The fast growth of the Internet of Things (IoT) and its diverse applications increase the risk of cyberattacks, one type of which is malware attacks. Due to the IoT devices’ different capabilities and the dynamic and ever-evolving environment, applying complex security measures is challenging, and applying only basic security standards is risky. Artificial Immune Systems (AIS) are intrusion-detecting algorithms inspired by the human body’s adaptive immune system techniques. Most of these algorithms imitate the human’s body B-cell and T-cell defensive mechanisms. They are lightweight, adaptive, and able to detect malware attacks without prior knowledge. In this work, we review the recent advances in employing AIS for the improved detection of malware in IoT networks. We present a critical analysis that highlights the limitations of the state-of-the-art in AIS research and offer insights into promising new research directions.

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Internet of Things (IoT) devices characterized by low power and low processing capabilities do not exactly fit into the provision of existing security techniques, due to their constrained nature. Classical security algorithms which are built on complex cryptographic functions often require a level of processing that low power IoT devices are incapable to effectively achieve due to limited power and processing resources. Consequently, the option for constrained IoT devices lies in either developing new security schemes or modifying existing ones to be more suitable for constrained IoT devices. In this work, an Efficient security Algorithm for Constrained IoT devices; based on the Advanced Encryption Standard is proposed. We present a cryptanalytic overview of the consequence of complexity reduction together with a supporting mathematical justification, and provisioned a secure element (ATECC608A) as a trade-off. The ATECC608A doubles for authentication and guarding against implementation attacks on the associated IoT device (ARM Cortex M4 microprocessor) in line with our analysis. The software implementation of the efficient algorithm for constrained IoT devices shows up to 35% reduction in the time it takes to complete the encryption of a single block (16bytes) of plain text, in comparison to the currently used standard AES-128 algorithm, and in comparison to current results in literature at 26.6%