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An Automatic Parameter Optimizing Scheme for RocksDB
Jiwon Kim, Hyeonmyeong Lee, Sungmin Jung, Heeseung Jo
http://doi.org/10.5626/JOK.2021.48.11.1167
For users with low understanding of application, it is very difficult to optimize a complex application. Leading studies that optimize application using one or two parameters can enhance the performance of an application. However, it is difficult to consider the relationship between various parameters using a single parameter optimization. In this paper, we proposed two techniques, LDH-Force and PF-LDH, that could optimize several parameters at the same time. The LDH-Force technique could efficiently reduce the number of searches by adding an LDH process, while simultaneously finding the optimal parameter combination for several parameters. The PF-LDH technique could further reduce the search cost by adding a filtering process and confirming that the degree to which the parameter affects the performance is different. Evaluation results confirmed that the proposed scheme had performance improvement of up to 42.55 times. The proposed scheme was able to find the optimal parameter combination at the lowest search cost without user intervention under various workloads.
V-gram: Malware Detection Using Opcode Basic Blocks and Deep Learning
Seongmin Jeong, Hyeonseok Kim, Youngjae Kim, Myungkeun Yoon
http://doi.org/10.5626/JOK.2019.46.7.599
With the rapid increase in number of malwares, automatic detection based on machine learning becomes more important. Since the opcode sequence extracted from a malicious executable file is useful feature for malware detection, it is widely used as input data for machine learning through byte-based n-gram processing techniques. This study proposed a V-gram, a new data preprocessing technique for deep learning, which improves existing n-gram methods in terms of processing speed and storage space. V-gram can prevent unnecessary generation of meaningless input data from opcode sequences. It was verified that the V-gram is superior to the conventional n-gram method in terms of processing speed, storage space, and detection accuracy, through experiments conducted by collecting more than 64,000 normal and malicious code files.
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