Application of Machine Learning in Intrusion Detection Systems
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-37717-4_86
Reference81 articles.
1. Liu, J., Xiao, K., Luo, L., Li, Y., Chen, L.: An intrusion detection system integrating network-level intrusion detection and host-level intrusion detection. In: 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS), pp. 122–129 (2020). https://doi.org/10.1109/QRS51102.2020.00028
2. Ring, M., Wunderlich, S., Scheuring, D., Landes, D., Hotho, A.: A survey of network-based intrusion detection data sets. Comput. Secur. 86, 147–167 (2019). https://doi.org/10.1016/j.cose.2019.06.005
3. Liu, H., Lang, B.: Machine learning and deep learning methods for intrusion detection systems: a survey. Appl. Sci. 9(20), 4396 (2019). https://doi.org/10.3390/app9204396
4. Alpeev, Y.V., Stadnik, A.N., Korolev, I.D.: 2020 International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE) – Analysis of Methods and Systems of Computer Attacks Detection and Probabilistic Representation of Conditions to Respond, Moscow, Russia (2020). https://doi.org/10.1109/REEPE49198.2020.9059126
5. Wu, Y., Wei, D., Feng, J.: Network attacks detection methods based on deep learning techniques: a survey. Secur. Commun. Netw., Article ID 8872923, 17 p. (2020). https://doi.org/10.1155/2020/8872923
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