Linear adversarial vector modeling based intrusion detection using feature subset selection and representation methods
Author:
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Safety, Risk, Reliability and Quality
Link
https://link.springer.com/content/pdf/10.1007/s13198-023-02030-y.pdf
Reference21 articles.
1. Beigh BM, Peer MA (2014) Performance evaluation of different intrusion detection system: an empirical approach. In: Intl conf. on computer communication and informatics, pp 1–7
2. Buczak AL, Guven E (2016) A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor 18(2):1153–1176
3. Cao J, Wu Z, Mao B, Zhang Y (2013) Shilling attack detection utilizing semi-supervised learning method for attack detection utilizing semi-supervised learning method for collaborative recommender system. World Wide Web J 16(5–6):729–748
4. Di Mauro M, Galatro G, Liotta A (2020) Experimental review of neural-based approaches for network intrusion management. IEEE Trans Netw Serv Manag 17:2480–2495
5. Gouveia, A., & Correia, M. (2017, May). A systematic approach for the application of restricted Boltzmann machines in network intrusion detection. In International Work-Conference on Artificial Neural Networks (pp. 432–446). Cham: Springer International Publishing.
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