과제정보
본 논문은 2021년 정부(국토교통부)의 재원으로 국토교통과학기술진흥원(KAIA)의 지원을 받아 연구가 수행된 연구임(21TLRP-B152768-03, 자율협력주행 도로교통체계 통합보안시스템 운영을 위한 기술 및 제도개발).
참고문헌
- TAN X P, SU S J, HUANG Z P, et al. Wireless sensor networks intrusion detection based on SMOTE and the random forest algorithm. Sensors, 2019, 19(1): 203. https://doi.org/10.3390/s19010203
- LI C L, LIU S G. A comparative study of the class imbalance problem in Twitter spam detection. Concurrency and Computation: Practice and Experience, 2017, 30(5): e4281. https://doi.org/10.1002/cpe.4281
- LI Y L, SUN G S, ZHU Y H. Data imbalance problem in text classification. Proc. of the 3rd International Symposium on Information Processing, 2010: 301 - 305.
- ZHU M, XIA J, JIN X Q, et al. Class weights random forest algorithm for processing class imbalanced medical data. IEEE Access, 2018, 6: 4641-4652. https://doi.org/10.1109/access.2018.2789428
- Yan, B.; Han, G.; Sun, M.; Ye, S. A Novel Region Adaptive SMOTE Algorithm for Intrusion Detection on Imbalanced Problem. In Proceedings of the 2017 3rd IEEE International Conference on Computer and Communications (ICCC); IEEE: Chengdu, December 2017; pp. 1281-1286.
- H. Haibo , A. Garcia, E.: "Learning from Imbalanced Data", IEEE Transactions On Knowledge And Data Engineering, Vol.2, No.9, September (2009).
- HAN H, WANG W Y, MAO B H. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. Proc. of the International Conference on Advances in Intelligent Computing, 2005: 878 - 887.
- CHAWLA N V, LAZAREVIC A, HALL L O, et al. SMOTE- Boost: improving prediction of the minority class in boosting. Proc. of the 7th European Conference on Principles and Prac- tice of Knowledge Discovery in Databases, 2003: 107 - 119.
- FREUND Y. Experiment with a new boosting algorithm. Proc. of the 13th International Conference on Machine Learning, 1996: 148 - 156.
- Yong Sun; Feng Liu SMOTE-NCL: A Re-Sampling Method with Filter for Network Intrusion Detection. In Proceedings of the 2016 2nd IEEE International Conference on Computer and Communications (ICCC); IEEE: Chengdu, China, October 2016; pp. 1157-1161.
- 정일옥, 전이학습과 불균형 데이터 처리를 통한 침입탐지 성능향상에 관한 연구, 박사학위논문, 고려대학교 2021. 8
- Leea, H.J.; Lee, S. 데이터 전처리와 앙상블 기법을 통한 불균형 데이터의 분류모형 비교 연구. 응용통계연구 2014, 27, 357-371, doi:10.5351/KJAS.2014.27.3.357.
- Son, M.J.; Jung, S.W.; Hwang, E.J. 불균형 데이터 분류를 위한 딥러닝 기반 오버샘플링 기법. 정보처리학회논문지:소프트웨어 및 데이터공학 2019, 8, 311-316, doi:10.3745/KTSDE.2019.8.7.311.
- Kim, D.; Kang, S.; Song, J. 불균형 자료에 대한 분류분석. 응용통계연구 2015, 28, 495-509, doi:10.5351/KJAS.2015.28.3.495.
- M. Kubat and S. Matwin, "Addressing the curse of imbalanced training sets: one-sided selection," in Proceedings of the International Conference on Machine Learning, pp. 179-186, Nashville, Tenn, USA, 1997.
- Y. Liu, X. H. Yu, J. X. Huang, and A. J. An, "Combining integrated sampling with SVM ensembles for learning from imbalanced datasets," Information Processing & Management, vol. 47, no. 4, pp. 617-631, 2011. https://doi.org/10.1016/j.ipm.2010.11.007
- Csic torpeda 2012, http data sets, July 20, 2021. [Online]. Available: http://www.tic.itefi.csic.es/torpeda.
- Carmen Torrano-Gimenez, Alejandro Perez-Villegas, and Gonzalo Alvarez. "TORPEDA: Una Especificacion Abierta de Conjuntos de Datos para la Evaluacion de Cortafuegos de Aplicaciones Web." 2012. TIN2011-29709-C0201.
- Web Attacks Detection based on CNN - Csic torpedo 2012 http data sets - GitHub, July 20, 2021. [Online]. Available: https://github.com/DuckDuckBug/cnn_waf.