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.
This study aims to develop an actively deformable space antenna system for radio astronomy around 100GHz. In order to reduce the thermal deformation in active control mechanisms composed of multiple materials, a method to cancel the mismatch of the coefficients of thermal expansion (CTE) is proposed and tested. The CTE mismatch between piezoelements and Super Invar structures is cancelled by adding another metallic part. For effective CTE cancellation, this study clarifies the cause of thermal deformation induced by the preloading mechanisms for a piezoelectric stack actuator.In addition, it shows the effect of the dimension tolerance of the piezoelements is significant, but still manageable by the proposed CTE cancellation method. The effectiveness of proposed method is validated by the use of finite-element analysis, prototyping models, and thermal deformation measurement experiments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.