A Novel PCA-Logistic Regression for Intrusion Detection System-Reference-Cited by-同舟云学术

A Novel PCA-Logistic Regression for Intrusion Detection System

Published:2023 Issue: Volume: Page:575-588
ISSN:2367-3370
Container-title:Key Digital Trends Shaping the Future of Information and Management Science
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Author:

Ogundokun Roseline Oluwaseun^ORCID,Odusami Modupe,Sisodia Dilip Singh,Awotunde Joseph Bamidele^ORCID,Tiwari Damodar Prasad

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-31153-6_46

Reference23 articles.

1. Ogundokun, R.O., Awotunde, J.B., Sadiku, P., Adeniyi, E.A., Abiodun, M., Dauda, O.I.: An enhanced intrusion detection system using particle swarm optimization feature extraction technique. Procedia Comput. Sci. 193, 504–512 (2021)

2. Gaikwad, D.P., Thool, R.C.: Intrusion detection system using bagging with partial decision tree-based classifier. Procedia Comput. Sci. 49, 92–98 (2015)

3. Lin, W.C., Ke, S.W., Tsai, C.F.: CANN: an intrusion detection system based on combining cluster centers and nearest neighbors. Knowl.-Based Syst. 78, 13–21 (2015)

4. Thapa, S., Mailewa, A.: The role of intrusion detection/prevention systems in modern computer networks: a review. In Conference: Midwest Instruction and Computing Symposium (MICS), vol. 53, pp. 1–14 (2020)

5. Musa, U.S., Chhabra, M., Ali, A., Kaur, M.: Intrusion detection system using machine learning techniques: a review. In: 2020 International Conference on Smart Electronics and Communication (ICO SEC), pp. 149–155. IEEE (2020)

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