Abstract
Detection of malware signatures from executable files requires effective signal processing and sandboxing operations, wherein the executable file is scanned for any malignant behavior. The recent malware detection techniques are based on static approaches that use machine and deep learning for analyzing malware signatures from byte and assembly-level program data. The byte-patterns are based on outliers, and the program-data is classified as a malware. These methods are not capable of detecting new variants of malware with long patterns of codes and huge dataset to classify the benign or malicious files. The issue with pattern analysis of large byte code dataset needs effective classification performance. To overcome these drawbacks, this paper has proposed a novel fused-triple convolutional neural network (fCNN) based framework for malware detection. This framework improves the accuracy of malware classification by converting the byte and assembly information into image data. This framework obtained more than 98% accuracy on the Microsoft Malware Dataset.
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Smmarwar, S.K., Gupta, G.P., Kumar, S. (2021). Design of a Fused Triple Convolutional Neural Network for Malware Detection: A Visual Classification Approach. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_26
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