Abstract
A program or piece of code known as malware, sometimes known as malicious software, is intended to enter a computer system without the consent of the user, cause harm, or carry out any number of undesirable behaviours, such as interfering with computer functions, gathering sensitive data bypassing control and gain access to a private computer systems. They also corrupt websites and can deny user from using the website. Deep learning tecniques especially Convolutional Neural Networks have outperformed several other avant-garde learning models with regard to better analysis of long sequences of system calls and image classification. We have suggested a CNN-based architecture to categorise malware samples as a result of this achievement. Grayscale pictures created from malware binaries were then used to train a CNN model for classification. Additionally, while comparing the CNN and AlexNet models’ performance, In terms of high accuracy and low FPR, we discovered that the AlexNet model outperformed the CNN model.
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Sai Adhinesh Reddy, T., Varma Vadlamudi, V.S.Y., Acharya, S., Rawat, U., Bhatnagar, R. (2023). Windows Malware Detection Using CNN and AlexNet Learning Models. In: Hassanien, A.E., Snášel, V., Tang, M., Sung, TW., Chang, KC. (eds) Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022. AISI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-031-20601-6_25
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DOI: https://doi.org/10.1007/978-3-031-20601-6_25
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