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The gene encoding a ferredoxin (Fd) from Haloarculajaponica strain TR-1 was cloned and sequenced. Sequence analysis of the cloned Ha. japonica Fd gene revealed that the structural gene consisted of an open reading frame of 387 nucleotides encoding 129 amino acids. The deduced amino acid sequence of Ha. japonica Fd showed 84 to 98% identity with corresponding sequences in other extremely halophilic archaea. The Ha. japonica Fd gene was inserted into the shuttle vector pWL102 and used to transform Ha. japonica. Ha. japonica Fd could then be produced as a fusion with His-Tag (6xHis) in Ha. japonica host cells. The absorption and ESR spectra of the Fd/His Tag fusion protein revealed the presence of a [2Fe-2S] cluster which is characteristic of native Ha. japonica Fd.

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A malware detection algorithm that can be embedded in IoT edge computing is proposed in this study and validated using an emulator. This algorithm, with a pattern match accelerator, reduces the computing cost while maintaining a relatively high detection accuracy. For autonomous driving, complicated IoT edge computing must have a huge amount of embedded program codes. In such a situation, the invasion of malware can lead to compromised cybersecurity. In this study, a pattern match accelerator is implemented for such issues, thereby offering IoT edge computing that detects malware automatically. Edge computing is designed to apply simply structural level analysis algorithms using HLAC mask pattern. We developed a pseudo‐emulator system environment and conducted performance confirmation of the proposed technique using 641 chosen samples from six types of malware families. The algorithm's efficiencies demonstrated an identification performance of approximately 80%. In comparison to characteristic extraction using AI, the computing cost was reduced and these processes enable edge computing with high cybersecurity features.

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