Abstract: Due to financial incentives, the number of malware infections is steadily rising. Accuracy and effectiveness are essential because malware detection systems serve as the first line of defense against harmful attacks. A zero-day vulnerability is a hole in the target operating system, device driver, application, or other tools employing a computer environment that was previously unknown to anybody other than the hacker. Traditional malware detection systems usually use conventional machine learning algorithms, which call for time-consuming and error-prone feature gathering and extraction. Convolutional neural networks (CNNs) have been demonstrated to outperform conventional learning techniques in a number of applications, including the classification of images. This success prompts us to suggest a CNN-based malware categorization architecture. We evaluated our methodology using a bigger dataset made up of 25 families within a corpus of 9342 malware. Last but not least, comparisons are made between the model's measurement and performance with other cutting-edge deep learning techniques. The overall testing accuracy of 98.31% in the provided results attested to the excellent accuracy and robustness of the suggested procedure at a lower computational cost.

Mohamed Abo Rizka, Mohamed Hamed and Hatem A. Khater, “An Intelligent Malware Classification Model Based on Image Transformation” International Journal of Advanced Computer Science and Applications(IJACSA), 14(7), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140790

@article{Rizka2023,
title = {An Intelligent Malware Classification Model Based on Image Transformation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140790},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140790},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {7},
author = {Mohamed Abo Rizka and Mohamed Hamed and Hatem A. Khater}
}