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• W4224272896 abstract "The most often reported danger to computer security is malware. Antivirus company AV-Test Institute reports that more than 5 million malware samples are created each day. A malware classification method is frequently required to prioritize these occurrences because security teams cannot address all of that malware at once. Malware’s variety, volume, and sophistication are all growing at an alarming rate. Hackers and attackers routinely design systems that can automatically rearrange and encrypt their code to escape discovery. Traditional machine learning approaches, in which classifiers learn based on a hand-crafted feature vector, are ineffective for classifying malware. Recently, deep convolutional neural networks (CNNs) successfully identified and classified malware. To categorize malware, a smart system has been suggested in this research. A novel model of deep learning is introduced to categorize malware families and multiclassification. The malware file is converted to a grayscale picture, and the image is then classified using a convolutional neural network. To evaluate the performance of our technique, we used a Microsoft malware dataset of 10,000 samples with nine distinct classifications. The findings stood out among the deep learning models with 99.97% accuracy for nine malware types." @default.
• W4224272896 created "2022-04-26" @default.
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• W4224272896 date "2022-04-21" @default.
• W4224272896 modified "2023-09-25" @default.
• W4224272896 title "Digital Forensics for Malware Classification: An Approach for Binary Code to Pixel Vector Transition" @default.
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• W4224272896 doi "https://doi.org/10.1155/2022/6294058" @default.
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