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NTIS 바로가기한국정보통신학회논문지 = Journal of the Korea Institute of Information and Communication Engineering, v.26 no.12, 2022년, pp.1872 - 1879
강한바다 (Department of Convergence Security, Chung-Ang University) , 이재우 (Department of Industrial Security, Chung-Ang University)
Recently, with the development of artificial intelligence technology, research to use artificial intelligence to detect hacking attacks is being actively conducted. However, the fact that security data is a representative imbalanced data is recognized as a major obstacle in composing the learning da...
S. H Seo, Y. J. Jeon, J. S. Lee, H. J. Jung, and J. T. Kim, "An Over-sampling Method based on Generative Adversarial Networks for Effective Classification of Imbalanced Big Data," in Proceedings of Korea Software Congress 2017, Busan, Korea, pp. 1030-1032, 2017.
J. H. Yang, "Comparison of the Classification Algorithms Using a Sampling Technique in Imbalanced Data," M. S. thesis, Dongguk University, Korea, 2017.
I. O. Jung, J. W. Ji, G. H. Lee, and M. J. Kim, "A study on intrusion detection performance improvement through imbalanced data processing," Jouranl of Information and Security, vol. 21, no. 3, pp. 57-66, Sep. 2021.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique," Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, Jun. 2002.
H. He, Y. Bai, E. A. Garcia, and S. Li, "ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning," in Proceedings of IEEE International Joint Conference on Neural Networks, Hong Kong, pp.1322-1328, 2008.
K. Lee, "Oversampling based on Gaussian Mixture Model for Imbalanced data classification," M. S. thesis, Hanyang University, Korea, 2019.
Y. H. Choe and K. W. Oh, "A Study on the Introduction of CTGAN Oversampling Algorithm to improve Imbalance Problem in Intrusion Detection Data," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 12, pp. 2114-2122, Dec. 2020.
S. T. Yoo and K. S. Kim., "Comparison of Anomaly Detection Performance Based on GRU Model Applying Various Data Preprocessing Techniques and Data Oversampling," Journal of the Korea Institute of Information Security & Cryptology, vol. 32, no. 2, pp. 201-211, Apr. 2022.
D. P. Kingma and M. Welling, "Auto-Encoding Variational Bayes," arXiv:1312.6114v10, 2013.
J. H. Park, "Improving Fashion Style Classification Accuracy using VAE in Class Imbalance Problem," The Journal of Korean Institute of Information Technology, vol. 19, no. 2, pp. 1-10, Feb. 2021.
K. Sohn, H. Lee, and X. Yan, "Learning Structured Output Representation using Deep Conditional Generative Models," in Proceedings of Advances in neural information processing systems (NeurIPS), Montreal: QC, Canada, pp. 3483-3491, 2015.
F. Ulger, S. E. Yuksel, and A. Yilmaz, "Anomaly Detection for Solder Joints Using β-VAE," IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 11, no. 12, pp. 2214-2221, Oct. 2021.
H. Tingfei, C. Guangquan, and H. Kuihua, "Using Variational Auto Encoding in Credit Card Fraud Detection," IEEE Access, vol. 8, pp. 149841-149853, Aug. 2020.
S. C. Hsiao, D. Y. Kao, Z. Y. Liu, and R. Tso, "Malware Image Classification Using One-Shot Learning with Siamese Networks," in Procedia Computer Science, Budapest, Hungary, vol. 159, pp. 1863-1871, 2019.
University of new brunswick, NSK-KDD dataset [Online]. Available: https://www.unb.ca/cic/datasets/nsl.html.
P. Devan and N. Khare, "An efficient XGBoost-DNN-based classification model for network intrusion detection system," Neural Computing and Applications, vol. 32, pp. 12499-12514, Jan. 2020.
C. Yin, Y. Zhu, J. Fei and X. He, "A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks," IEEE Access, vol. 5, pp. 21954-21961, Oct. 2017.
K. J. Ryu, "Study for Solving Network Traffic Data Imbalance And Rare Class Problems Using a Similarity Neural Network," M. S. thesis, Sejong University, Korea, 2021.
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