참고문헌
- Y. Su, M. Li, C. Tang, and R. Shen, "A framework of apt detection based on dynamic analysis," 2015 4th National Conference on Electrical, Electronics and Computer Engineering, Atlantis Press, 2015.
- Y. G. Choi and S. S. Park, "Reinforcement Mining Method for Anomaly Detection and Misuse Detection using Postꠓ processing and Training Method," Proceedings of the Korean Information Science Society Conference, pp.238-240, 2006.
- S. O. Choi and W. N. Kim, "Control system intrusion detection system technology research trend," Review of Korea Institute of Information Security and Cryptology, Vol.24, No.5, pp.7-14, 2014.
- J. JP. Tsai and S. Y. Philip, "Machine learning in cyber trust: Security privacy and reliability," Springer Science & Business Media, 2009.
- S. X. Wu and W. Banzhaf, "The use of computational intelligence in intrusion detection systems: A review," Applied Soft Computing, Vol.10, No.1, pp.1-35, 2010. https://doi.org/10.1016/j.asoc.2009.06.019
- M. S. Iftikhar and M. R. Fraz, "A Survey on Application of Swarm Intelligence in Network Security," Transactions on Machine Learning and Artificial Intelligence, Vol.1, No.1, pp.1-15, 2013.
- T. Mehmood and HBM. Rais, "Machine learning algorithms in context of intrusion detection," 2016 3rd International Conference on Computer and Information Sciences (ICCOINS), Kuala Lumpur, pp.369-373, 2016.
- L. N. Tidjon, M. Frappier, and A. Mammar, "Intrusion detection systems: A cross-domain overview," IEEE Communications Surveys & Tutorials, Vol.21, No.4, pp.3639-3681, 2019. https://doi.org/10.1109/COMST.2019.2922584
- H. Kwon, Y. C. Kim, H. S. Yoon, and D. S. Choi, "Optimal cluster expansion-based intrusion tolerant system to prevent denial of service attacks," Applied Sciences, Vol.7, No.11, pp.1186, 2017. https://doi.org/10.3390/app7111186
- T. Mouttaqi, T. Rachidi, and N. Assem, "Re-evaluation of combined Markov-Bayes models for host intrusion detection on the ADFA dataset," 2017 Intelligent Systems Conference (IntelliSys), IEEE, 2017.
- O. Yavanoglu and M. Aydos, "A review on cyber security datasets for machine learning algorithms," 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, pp.2186-2193, 2017.
- M. Pendleton and S. Xu, "A dataset generator for next generation system call host intrusion detection systems," MILCOM 2017-2017 IEEE Military Communications Conference (MILCOM). IEEE, 2017.
- G. Creech and J. Hu, "A semantic approach to host-based intrusion detection systems using contiꠓguousand discontiguous system call patterns," IEEE Transactions on Computers, Vol.63, No.4, pp.807-819, 2013. https://doi.org/10.1109/TC.2013.13
- M. Xie and J. Hu, "Evaluating host-based anomaly detection systems: A preliminary analysis of adfa-ld," Image and Signal Processing (CISP), 2013 6th International Congress on. Vol.3. IEEE, 2013.
- P. Laskov, P. Dusse, C. Schafer, and K. Rieck, "Learning intrusion detection: supervised or unsupervised?," International Conference on Image Analysis and Processing. Springer, Berlin, Heidelberg, 2005.
- J. H. Kim and H. W. Kim, "An effective intrusion detection classifier using long short-term memory with gradient descent optimization," 2017 International Conference on Platform Technology and Service (PlatCon). IEEE, 2017.
- G. W. Kim, H. Y. Yi, J. H. Lee, Y. H. Paek, and S. R. Yoon, "LSTM-based system-call language modeling and robust ensemble method for designing host-based intrusion detection systems," arXiv preprint arXiv:1611.01726 (2016).
- R. D. Ravipati and M. Abualkibash, "Intrusion Detection System Classification Using Different Machine Learning Algorithms on KDD-99 and NSL-KDD Datasets-A Review Paper," International Journal of Computer Science & Information Technology (IJCSIT), Vol.11, 2019.
- M. M. Rohling, M. Grimmer, D. Kreubel, J. Hoffmann, and B. Franczyk, "Standardized container virtualization approach for collecting host intrusion detection data," 2019 Federated Conference on Computer Science and Information Systems (FedCSIS), IEEE, 2019.
- A. Khraisat, I. Gondal, P. Vamplew and J. Kamruzzaman, "Survey of intrusion detection systems: Techniques, datasets and challenges," Cybersecurity, Vol.2, No.1, pp.1-22, 2019. https://doi.org/10.1186/s42400-018-0018-3
- M. Grimmer, MM. Rohling, D. Kreubel and S. Ganz, "A modern and sophisticated host based intrusion detection data set," IT-Sicherheit als Voraussetzung Fur Eine Erfolgreiche Digitalisierung, pp.135-145, 2019.