Malicious code detection using deep learning based LSTM model-Reference-Cited by-同舟云学术

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Malicious code detection using deep learning based LSTM model

Published:2023 Issue: Volume: Page:
ISSN:0094-243X
Container-title:COMPUTATIONAL INTELLIGENCE AND NETWORK SECURITY
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Author:

Komanduri Sai Rama Krishna,Kanumalli Satya Sandeep,Majety Vasumathi Devi,Sujatha V.

Publisher

AIP Publishing

Link

http://aip.scitation.org/doi/pdf/10.1063/5.0137178

Reference27 articles.

1. Tobiyama, S., Yamaguchi, Y., Shimada, H., Ikuse, T., & Yagi, T. (2016, June). Malware detection with deep neural network using process behavior. In Computer Software and Applications Conference (COMPSAC), 2016 IEEE 40th Annual (Vol. 2, pp. 577–582). IEEE.

2. Huang, W., & Stokes, J. W. (2016, July). MtNet: a multi-task neural network for dynamic malware classification. In International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (pp. 399–418). Springer, Cham.

3. Pascanu, R., Stokes, J. W., Sanossian, H., Marinescu, M., & Thomas, A. (2015, April). Malware classification with recurrent networks. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on (pp. 1916–1920). IEEE.

4. Shibahara, T., Yagi, T., Akiyama, M., Chiba, D., & Yada, T. (2016, December). Efficient dynamic malware analysis based on network behavior using deep learning. In Global Communications Conference (GLOBECOM), 2016 IEEE (pp. 1–7). IEEE.

5. Kolosnjaji, B., Zarras, A., Webster, G., & Eckert, C. (2016, December). Deep learning for classification of malware system call sequences. In Australasian Joint Conference on Artificial Intelligence (pp. 137–149). Springer, Cham.

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1. Enhanced Malware Detection using Convolutional Neural Network with Robust Ensemble Algorithm-"LIGHTGBM";2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE);2024-01-24

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