Literature Review of Visualizing and Detecting Malware

Zhaoheng Yin

doi:10.47297/taposatWSP2633-456921.20230402

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Literature Review of Visualizing and Detecting Malware

4卷第2期

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Literature Review of Visualizing and Detecting Malware
Literature Review of Visualizing and Detecting Malware
School of Electrical Engineering and Computer Science, University of Ottawa,, ON,Ottawa,Canada,K1N 6N5
2023年4卷第2期页码：137-142
纸质出版日期： 2023 ，
DOI：10.47297/taposatWSP2633-456921.20230402

Full txt

(2023). Literature Review of Visualizing and Detecting Malware. 科技理论与实践（英文版）, 4(2), 137-142.

Zhaoheng Yin. (2023). Literature Review of Visualizing and Detecting Malware. Theory and Practice of Science and Technology, 4(2), 137-142.
(2023). Literature Review of Visualizing and Detecting Malware. 科技理论与实践（英文版）, 4(2), 137-142. DOI： 10.47297/taposatWSP2633-456921.20230402.

Zhaoheng Yin. (2023). Literature Review of Visualizing and Detecting Malware. Theory and Practice of Science and Technology, 4(2), 137-142. DOI： 10.47297/taposatWSP2633-456921.20230402.

摘要

Abstract

Visualization detection is an emerging technology by extracting the characteristics of malware image performance

has solved the traditional detection technology can not accurately detect the unknown malicious code problem

the method has received more and more attention

especially in the context of the continuous development of anti-detection technology

this paper on the different types of malware code visualization detection methods to conduct a comprehensive research and analysis from the grayscale image transformation

machine learning visualization Deep Learning Visualization and Hybrid Detection

summarizes and analyzes the current problems and future development direction of visual detection

aiming to help scholars improve the accuracy and efficiency of visual detection methods.

关键词

Keywords

Visualizing detectionMalwareDeep leaningMachine learning

references

Nataraj, L., Karthikeyan, S., Jacob, G., & Manjunath, B. S. (2011, July). Malware images: visualization and automatic classification. In Proceedings of the 8th international symposium on visualization for cyber security, 1-7.[2] Conti, G., Bratus, S., Shubina, A., Sangster, B., Ragsdale, R., Supan, M., ... & Perez-Alemany, R. (2010). Automated mapping of large binary objects using primitive fragment type classification. digital investigation, 7, S3-S12.[3] Han, K. S., Lim, J. H., Kang, B., & Im, E. G. (2015). Malware analysis using visualized images and entropy graphs. International Journal of Information Security, 14, 1-14.[4] Chen, L., Sahita, R., Parikh, J., & Marino, M. (2020). STAMINA: Scalable Deep Learning Approach for Malware Classification.[5] Han, K., Kang, B., & Im, E. G. (2014). Malware analysis using visualized image matrices. The Scientific World Journal, 2014.[6] Sun, B., Zhang, P., Cheng, M., Li, X., Li, Q. (2020). Malware detection method based on enhanced code images. Journal of Tsinghua University (Science and Technology), 60(5), 386-92.[7] Sun, G., Qian, Q. (2018). Deep learning and visualization for identifying malware families. IEEE Transactions on Dependable and Secure Computing, 18(1), 283-95.[8] Aslan, Ö., & Yilmaz, A. A. (2021). A new malware classification framework based on deep learning algorithms. Ieee Access, 9, 87936-87951.[9] Vasan, D., Alazab, M., Wassan, S., Safaei, B., & Zheng, Q. (2020). Image-Based malware classification using ensemble of CNN architectures (IMCEC). Computers & Security, 92, 101748.[10]Liu, S., Maljovec, D., Wang, B., Bremer, P. T., & Pascucci, V. (2016). Visualizing high-dimensional data: Advances in the past decade. IEEE transactions on visualization and computer graphics, 23(3), 1249-68.[11]Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.[12]Vasan, D., Alazab, M., Wassan, S., Safaei, B., & Zheng, Q. (2020). Image-Based malware classification using ensemble of CNN architectures (IMCEC). Computers & Security, 92, 101748.[13]Li, T., Chang, H., Mishra, S., Zhang, H., Katabi, D., & Krishnan, D. (2023). Mage: Masked generative encoder to unify representation learning and image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2142-52.

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