In recent years, the increase in non-Windows malware threats had turned the focus of the cybersecurity community. Research works on hunting Windows PE-based malwares are maturing, whereas the developments on Linux malware threat hunting are relatively scarce. With the advent of the Internet of Things (IoT) era, smart devices that are getting integrated into human life have become a hackers' highway for their malicious activities. The IoT devices employ various Unix-based architectures that follow ELF (Executable and Linkable Format) as their standard binary file specification. This study aims at providing a comprehensive survey on the latest developments in cross-architectural IoT malware detection and classification approaches. Aided by a modern taxonomy, we discuss the feature representations, feature extraction techniques, and machine learning models employed in the surveyed works. We further provide more insights on the practical challenges involved in cross-architectural IoT malware threat hunting and discuss various avenues to instill potential future research.

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Parallel computing has gained a great influence on scientific researches and in our daily life, especially when dealing with big data. One of the preconditions of high performance on computing is the support of efficient algorithms, which should be divisible and computing simultaneously. But not all algorithms are applicable for parallel computing, sometimes it can only make use of one single processor. In order to take full advantages of cluster or Multi-core CPUs in that case, A pipeline computation model is proposed which applies on cluster to make procedures more efficient and make full use of computer resources. Especially, our model has a very good performance on medical image process. With the model, almost all the positions of the organs in CT-images of a person could be found out simultaneously and accurately in one time, which can efficiently speed up the diagnosis of doctors, rather than the serial algorithm which can only find the position of one organ in one time before. The result of our experiment shows that the performance of the former serial algorithm has been improved by 40 percent by using our method Index Terms-Parallel computing, medical image, pipeline computation model.

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