A Malicious Code Family Classification Method Based on RGB Images and Lightweight Model
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8181-6_12
Reference25 articles.
1. Chaganti, R., Ravi, V., Pham, T.D.: Image-based malware representation approach with efficient net convolutional neural networks for effective malware classification. J. Inf. Secur. Appl. 69, 103306 (2022)
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3. Jian, Y., Kuang, H., Ren, C., Ma, Z., Wang, H.: A novel framework for image-based malware detection with a deep neural network. Comput. Secur. 109, 102400 (2021)
4. Kumar, S., Janet, B.: DTMIC: deep transfer learning for malware image classification. J. Inf. Secur. Appl. 64, 103063 (2022)
5. Kumar, S., et al.: MCFT-CNN: malware classification with fine-tune convolution neural networks using traditional and transfer learning in internet of things. Futur. Gener. Comput. Syst. 125, 334–351 (2021)
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