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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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Classification Hardness for Supervised Learners on 20 Years of Intrusion Detection Data., , , and . IEEE Access, (2019)Unsupervised Machine Learning Techniques for Network Intrusion Detection on Modern Data., , , , and . CSNet, page 1-8. IEEE, (2020)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)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)Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection., , , , and . J. Cybersecur. Priv., 3 (2): 118-144 (April 2023)A Novel Multi-Stage Approach for Hierarchical Intrusion Detection., , , , , , and . IEEE Trans. Netw. Serv. Manag., 20 (3): 3915-3929 (September 2023)Establishing the Contaminating Effect of Metadata Feature Inclusion in Machine-Learned Network Intrusion Detection Models., , , , and . DIMVA, volume 13358 of Lecture Notes in Computer Science, page 23-41. Springer, (2022)