Abstract
Classification of malware variants is the most challenging task in the cybersecurity landscape. Malware developers keep one step ahead of defenders for the sake of using advanced artificial intelligence techniques. That is why we are in extreme need of an efficient malware classifier. In this paper, we proposed and experiment a malware classifier able to affect each inputted malware into its corresponding family. To do so, we use the multi-layer perceptron algorithm with malware visualization technique. It refers to converting a malware binary into grayscale images. Besides, to reach a great accuracy, we experiment different architectures by varying hidden layers, neurons and activation functions. Then, we obtained an accuracy of 97.6%. At the end, we compare the obtained results with literature, and we conclude that the multi-layer perceptron algorithm is a good malware classifier with specified hyperparameters that were used.
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References
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Acknowledgements
We acknowledge financial support for this research from the “Centre National pour la Recherche Scientifique et Technique”, CNRST, Morocco.
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Ben Abdel Ouahab, I., Elaachak, L., Bouhorma, M. (2022). Image-Based Malware Classification Using Multi-layer Perceptron. In: Ben Ahmed, M., Teodorescu, HN.L., Mazri, T., Subashini, P., Boudhir, A.A. (eds) Networking, Intelligent Systems and Security. Smart Innovation, Systems and Technologies, vol 237. Springer, Singapore. https://doi.org/10.1007/978-981-16-3637-0_32
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DOI: https://doi.org/10.1007/978-981-16-3637-0_32
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