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Investigating Generalized Performance of Data-Constrained Supervised Machine Learning Models on Novel, Related Samples in Intrusion Detection.

, , , , and . Sensors, 23 (4): 1846 (February 2023)

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Towards Model Generalization for Intrusion Detection: Unsupervised Machine Learning Techniques., , , , and . J. Netw. Syst. Manag., 30 (1): 12 (2022)Performance Impact of Queue Sorting in Container-Based Application Scheduling., , , , , and . CNSM, page 1-9. IEEE, (2023)Task Assignment and Capacity Allocation for ML-Based Intrusion Detection as a Service in a Multi-Tier Architecture., , , , , , , and . IEEE Trans. Netw. Serv. Manag., 20 (1): 672-683 (March 2023)Discovering Non-Metadata Contaminant Features in Intrusion Detection Datasets., , , , and . PST, page 1-11. IEEE, (2022)Machine learning based intrusion detection as a service: task assignment and capacity allocation in a multi-tier architecture., , , , , , , and . UCC Companion, page 27:1-27:6. ACM, (2021)Unsupervised Machine Learning Techniques for Network Intrusion Detection on Modern Data., , , , and . CSNet, page 1-8. IEEE, (2020)Hierarchical feature block ranking for data-efficient intrusion detection modeling., , , , and . Comput. Networks, (2021)Investigating Generalized Performance of Data-Constrained Supervised Machine Learning Models on Novel, Related Samples in Intrusion Detection., , , , and . Sensors, 23 (4): 1846 (February 2023)Castles Built on Sand: Observations from Classifying Academic Cybersecurity Datasets with Minimalist Methods., , , , and . IoTBDS, page 61-72. SCITEPRESS, (2023)Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection., , , , and . J. Cybersecur. Priv., 3 (2): 118-144 (April 2023)