Comparative analysis of novel gradient boosting algorithm and recurrent neural network algorithms for malware detection and classification
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AIP Publishing
Reference16 articles.
1. M. Oyler-Castrillo, N. B. Agostini, G. Sznaier, and D. Kaeli, “Machine Learning-Based Malware Detection Using Recurrent Neural Networks,” in 2019 IEEE MIT Undergraduate Research Technology Conference (URTC) (2019).
2. V. J. L. Engel, M. M. Engel, and E. Joshua, “Neural Network with Principal Component Analysis for Malware Detection Using Network Traffic Features,” in Proceedings of the International Conferences on Information System and Technology (2019).
3. B. Alsulami, and S. Mancoridis, “Behavioral Malware Classification Using Convolutional Recurrent Neural Networks,” in 2018 13th International Conference on Malicious and Unwanted Software (MALWARE) (2018).
4. R. R. Curtin, A. B. Gardner, S. Grzonkowski, A. Kleymenov, and A. Mosquera, “Detecting DGA Domains with Recurrent Neural Networks and Side Information,” in Proceedings of the 14th International Conference on Availability, Reliability and Security (2019).
5. M. Almahmoud, D. Alzu’bi, and Q. Yaseen, “ReDroidDet: Android Malware Detection Based on Recurrent Neural Network,” Procedia Computer Science (2021).
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