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- W4313563103 abstract "Detecting malwares has always been a challenging task for malware analysts due to ever-changing technologies. To tackle advances in modern malware, which even have the potential to take entire organisations down within seconds, we resort to Machine and Deep Learning (ML/DL) to improve the detection process. This helps detect malware efficiently and accurately, without manual intervention, which often leads to overlooking advanced or obfuscated malwares. The objective of this paper is to train and validate various deep learning neural networks, such as convolutional and recurrent networks, while visualising the malware for better analysis at the same time. We also perform a comparative analysis based on various performance metrics. We were able to achieve over 98% validation accuracy using CNN. RNN yielded poor results. Although better accuracy has been achieved using other networks, this paper mainly focuses on a novel technique of visualising malware and using CNNs for detection." @default.
- W4313563103 created "2023-01-06" @default.
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- W4313563103 date "2022-11-26" @default.
- W4313563103 modified "2023-09-30" @default.
- W4313563103 title "A Unique Approach to Malware Detection Using Deep Convolutional Neural Networks" @default.
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- W4313563103 doi "https://doi.org/10.1109/icecie55199.2022.10000344" @default.
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