Acknowledgement
본 논문은 2024년도 산업통상자원부 및 한국산업기술진흥원의 산업혁신인재성장지원사업 (RS-2024-00415520)과 과학기술정보통신부 및 정보통신기획평가원의 ICT혁신인재4.0 사업의 연구결과로 수행되었음(No. IITP-2022-RS-2022-00156310)
References
- Aslan, Omer Aslan, and Refik Samet. "A comprehensive review on malware detection approaches." IEEE access 8 (2020): 6249-6271.
- Wang, Fangwei, et al. "An efficient deep unsupervised domain adaptation for unknown malware detection." Symmetry 14.2 (2022): 296.
- Pitolli, Gregorio, et al. "MalFamAware: automatic family identification and malware classification through online clustering." International Journal of information security 20 (2021): 371-386.
- Yang, Jian, et al. "Conditional variational auto-encoder and extreme value theory aided two-stage learning approach for intelligent fine-grained known/unknown intrusion detection." IEEE Transactions on Information Forensics and Security 16 (2021): 3538-3553.
- Soltani, Mahdi, et al. "An adaptable deep learning-based intrusion detection system to zero-day attacks." Journal of Information Security and Applications 76 (2023): 103516.
- Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, "Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization", 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018