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Malware Detection Method Based on Visualization

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Algorithms and Architectures for Parallel Processing (ICA3PP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14492))

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Abstract

The rapid development of information technology and computer networks has led to the emergence of various new applications on both PC platforms and mobile devices. Malware continues to evolve and update, which often developing new variants or changing existing features to evade detection. Traditional feature based malware detection methods are limited in their ability to detect variants, and are computationally resource-intensive. Considering these issues, a new visualization-based and integrated malware detection method, Mal_Vis, is introduced. It decompiles the application software and applies PCA to reduce the feature dimension, then visualises the decompiled data to greyscale and RGB image. A Stacking-based ensemble machine learning algorithm is used to classify the visualized images to detect malware. Experiments show the method achievs detection accuracy of 98.19% and 93.03% in the Windows and Android application software datasets.

This work was supported in part by the Science and Technology Research Project of the Education Department of Jilin Province under Grant No. JJKH20230850KJ, and the Science and Technology Development Plan Project of Jilin Province under Grant No. 20230508096RC.

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Correspondence to Haoxiang Liang .

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Xie, N., Liang, H., Mu, L., Zhang, C. (2024). Malware Detection Method Based on Visualization. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14492. Springer, Singapore. https://doi.org/10.1007/978-981-97-0811-6_15

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  • DOI: https://doi.org/10.1007/978-981-97-0811-6_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0810-9

  • Online ISBN: 978-981-97-0811-6

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