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- W4285331410 abstract "Malware is malicious software designed to damage, steal important information or data, interfere with computer performance, and other criminal acts on computers or devices that can harm users. To prevent the spread and harm caused by malware, there are various methods such as using machine learning to detect and classify software suspected of being malware. The malware analysis method consists of a static method, where the suspected malware is not executed and a dynamic method, when the suspicious is run to see and analyze its behavior. We propose different malware analysis method, namely using program visualization and image processing. This paper aims to explain the process of classifying malware using machine learning methods based on image processing. The steps taken are to convert the software program suspected of being malware into binary bits, then convert them into strings, 8- bit vectors, and then into grayscale images. Convolutional Neural Network (CNN) is used to process malware visualization datasets so that similarities can be found. The final model is expected to identify malware into one of the categories/families of an operating system. Parameter testing carried out in the form of measurement of accuracy, error, precision, and sensitivity of the model using a confusion matrix. Accuracy of the proposed system is 94%." @default.
- W4285331410 created "2022-07-14" @default.
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- W4285331410 date "2021-11-18" @default.
- W4285331410 modified "2023-10-16" @default.
- W4285331410 title "Classification of Malware Using Machine Learning Based on Image Processing" @default.
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- W4285331410 doi "https://doi.org/10.1109/tssa52866.2021.9768222" @default.
- W4285331410 hasPublicationYear "2021" @default.
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