최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기情報保護學會論文誌 = Journal of the Korea Institute of Information Security and Cryptology, v.32 no.2, 2022년, pp.201 - 211
유승태 (아주대학교 대학원 지식정보공학과) , 김강석 (아주대학교 사이버보안학과)
According to the recent change in the cybersecurity paradigm, research on anomaly detection methods using machine learning and deep learning techniques, which are AI implementation technologies, is increasing. In this study, a comparative study on data preprocessing techniques that can improve the a...
K. Rahul-Vigneswaran, P. Poornachandran, and KP. Soman, "Acompendium on network and host based intrusion detection systems," Proceedings of the 1st International Conference on Data Science, Machine Learning and Applications, pp. 23-30, May 2020.
Yun-gyung Cheong, Ki-namPark,Hyun-joo Kim, Jong-hyun Kim and Sang-won Hyun, "Machine learning based intrusion detection systems for class imbalanced datasets," Electronics and Telecommunications Research Institute, 27(6), pp. 1385-1395, Dec. 2017.
S. Mishra, "Handling imbalanced data: SMOTE vs. random under sampling," International Research Journal of Engineering and Technology, vol. 4, no. 8, pp. 317-320, Aug. 2017.
A. Radford, L. Metz and S. Chintala,"Unsupervised representation learning with deep convolutional generative adversarial networks," International Conference on Learning Representations, pp. 1-16, Jan. 2016.
Hyun Kwon, Seung-ho BangandKi-woong Park, "A design of deep neural network-based network intrusion detection system," Journal of KING Computing, 16(1), pp. 7-18, Feb. 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, Nov. 2017.
Jae-hyun Seo, "A comparative study on the classification of the imbalanced intrusion detection dataset based on deep learning," Journal of Korean Institute of Intelligent System, 28(2), pp. 152-159, April 2018.
M. Ramaiah, V. Chandrasekaran, V. Ravi and N. Kumar, "An intrusion detection system using optimized deep neural network architecture," Transactions on Emerging Telecommunications Technologies, vol. 32, no. 4, pp. 1-17, Feb. 2021.
R. Corizzo, E. Zdravevski, M. Russell, A. Vagliano and N. Japkowicz, "Feature extraction based on word embedding models for intrusion detection in network traffic," Journal of Surveillance, Security and Safety, vol. 1, pp. 140-150, Dec. 2020.
A. M. Dai, C. Olah, and Q. V. Le, "Document embedding with paragraph vectors," arXiv:1507.07998, 2015.
W. Haider, J. Hu, J. Slay, B.P. Turnbull and Y. Xie, "Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling," Journal of Network and Computer Applications, vol. 87, no. 1, pp. 185-192, June 2017.
H. Akoglu, "User's guide to correlation coefficients," Turkish Journal of Emergency Medicine, vol. 18, no. 3, pp. 91-93, Aug. 2018.
N. Quang-Hung, H. Doan and N.Thoai, "Performance evaluation of distributed training in tensorflow 2," International Conference on Advanced Computing and Applications, pp.155-159, Nov. 2020.
A. Ng, "Sizeof dev and test sets(C3W1L06)," 2017. https://github.com/hithesh111/Hith100/blob/master/100Days/day035.ipynb
R. A. Maxion and R. R. Roberts,"Proper Use of ROC Curves in Intrusion / Anomaly Detection," University of Newcastle upon Tyne, Computing Science Tyne, UK, p. 33, 2004.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Free Access. 출판사/학술단체 등이 허락한 무료 공개 사이트를 통해 자유로운 이용이 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.