Machine Learning-Based Hybrid Feature Selection for Improvised Network Intrusion Detection
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-23233-6_25
Reference18 articles.
1. Ahanger, A.S., Khan, S.M., Masoodi, F.: An effective intrusion detection system using supervised machine learning techniques. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE (2021)
2. Bandyopadhyay, S., et al.: A Decision Tree Based Intrusion Detection System for Identification of Malicious Web Attacks (2020)
3. Alabdulwahab, S., Moon, B.: Feature selection methods simultaneously improve the detection accuracy and model building time of machine learning classifiers. Symmetry 12(9), 1424 (2020)
4. Albulayhi, K., et al.: IoT intrusion detection using machine learning with a novel high performing feature selection method. Appl. Sci. 12(10), 5015 (2022)
5. Frank, E., et al.: Weka-a machine learning workbench for data mining. In: Data Mining and Knowledge Discovery Handbook, pp. 1269–1277. Springer (2009). https://doi.org/10.1007/978-0-387-09823-4_66
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