과제정보
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A1032361).
참고문헌
- Raghuraman, Chandni, et al. "Static and dynamic malware analysis using machine learning." First International Conference on Sustainable Technologies for Computational Intelligence: Proceedings of ICTSCI 2019. Springer Singapore, 2020.
- Aslan, Omer, and Abdullah Asim Yilmaz. "A new malware classification framework based on deep learning algorithms." Ieee Access 9 (2021): 87936-87951. https://doi.org/10.1109/ACCESS.2021.3089586
- Olowoyo, Olufikayo, and Pius Owolawi. "Malware classification using deep learning technique." 2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC). IEEE, 2020.
- Li, Chen, and Junjun Zheng. "API call-based malware classification using recurrent neural networks." Journal of Cyber Security and Mobility 10.3 (2021): 617-640. https://doi.org/10.13052/jcsm2245-1439.1036
- Rathore, Hemant, et al. "Malware detection using machine learning and deep learning." Big Data Analytics: 6th International Conference, BDA 2018, Warangal, India, December 18-21, 2018, Proceedings 6. Springer International Publishing, 2018.
- Catak, Ferhat Ozgur, et al. "Deep learning based Sequential model for malware analysis using Windows exe API Calls." PeerJ Computer Science 6 (2020): e285.
- Arik, Sercan O., and Tomas Pfister. "Tabnet: Attentive interpretable tabular learning. arXiv 2019." arXiv preprint arXiv:1908.07442 (1908).
- Soltanzadeh, Paria, and Mahdi Hashemzadeh. "RCSMOTE: Range-Controlled synthetic minority over-sampling technique for handling the class imbalance problem." Information Sciences 542 (2021): 92-111. https://doi.org/10.1016/j.ins.2020.07.014
- https://github.com/PacktPublishing/Mastering-Machine-Learning-for-Penetration-Testing/tree/master/Chapter03.