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ORCIDBruno Volckaert
- > Home > Persons > Bruno Volckaert
Publications
- 2023
[j68]Laurens D'hooge
, Miel Verkerken, Tim Wauters, Filip De Turck, Bruno Volckaert:
Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection. J. Cybersecur. Priv. 3(2): 118-144 (2023)- [j67]Laurens D'hooge, Miel Verkerken, Tim Wauters, Filip De Turck, Bruno Volckaert:
Investigating Generalized Performance of Data-Constrained Supervised Machine Learning Models on Novel, Related Samples in Intrusion Detection. Sensors 23(4): 1846 (2023) - [j65]Yuan-Cheng Lai, Didik Sudyana, Ying-Dar Lin, Miel Verkerken, Laurens D'hooge, Tim Wauters, Bruno Volckaert, Filip De Turck:
Task Assignment and Capacity Allocation for ML-Based Intrusion Detection as a Service in a Multi-Tier Architecture. IEEE Trans. Netw. Serv. Manag. 20(1): 672-683 (2023) - [j64]Miel Verkerken, Laurens D'hooge, Didik Sudyana, Ying-Dar Lin, Tim Wauters, Bruno Volckaert, Filip De Turck:
A Novel Multi-Stage Approach for Hierarchical Intrusion Detection. IEEE Trans. Netw. Serv. Manag. 20(3): 3915-3929 (2023) - [c95]José Santos, Miel Verkerken, Laurens D'hooge, Tim Wauters, Bruno Volckaert, Filip De Turck:
Performance Impact of Queue Sorting in Container-Based Application Scheduling. CNSM 2023: 1-9 - [c91]Laurens D'hooge, Miel Verkerken, Tim Wauters, Filip De Turck, Bruno Volckaert:
Castles Built on Sand: Observations from Classifying Academic Cybersecurity Datasets with Minimalist Methods. IoTBDS 2023: 61-72 - 2022
- [j59]Miel Verkerken, Laurens D'hooge, Tim Wauters, Bruno Volckaert, Filip De Turck:
Towards Model Generalization for Intrusion Detection: Unsupervised Machine Learning Techniques. J. Netw. Syst. Manag. 30(1): 12 (2022) - [c86]Laurens D'hooge, Miel Verkerken, Bruno Volckaert, Tim Wauters, Filip De Turck:
Establishing the Contaminating Effect of Metadata Feature Inclusion in Machine-Learned Network Intrusion Detection Models. DIMVA 2022: 23-41 - [c83]Laurens D'hooge, Miel Verkerken, Tim Wauters, Bruno Volckaert, Filip De Turck:
Discovering Non-Metadata Contaminant Features in Intrusion Detection Datasets. PST 2022: 1-11 - 2021
- [j52]Laurens D'hooge, Miel Verkerken, Tim Wauters, Bruno Volckaert, Filip De Turck:
Hierarchical feature block ranking for data-efficient intrusion detection modeling. Comput. Networks 201: 108613 (2021) - [c77]Yuan-Cheng Lai, Didik Sudyana, Ying-Dar Lin, Miel Verkerken, Laurens D'hooge, Tim Wauters, Bruno Volckaert, Filip De Turck:
Machine learning based intrusion detection as a service: task assignment and capacity allocation in a multi-tier architecture. UCC Companion 2021: 27:1-27:6 - 2020
- [j41]Laurens D'hooge, Tim Wauters, Bruno Volckaert, Filip De Turck:
Inter-dataset generalization strength of supervised machine learning methods for intrusion detection. J. Inf. Secur. Appl. 54: 102564 (2020) - [c74]Miel Verkerken, Laurens D'hooge, Tim Wauters, Bruno Volckaert, Filip De Turck:
Unsupervised Machine Learning Techniques for Network Intrusion Detection on Modern Data. CSNet 2020: 1-8 - 2019
- [j34]Laurens D'hooge, Tim Wauters, Bruno Volckaert, Filip De Turck:
Classification Hardness for Supervised Learners on 20 Years of Intrusion Detection Data. IEEE Access 7: 167455-167469 (2019) - [c63]Laurens D'hooge, Tim Wauters, Bruno Volckaert, Filip De Turck:
In-depth Comparative Evaluation of Supervised Machine Learning Approaches for Detection of Cybersecurity Threats. IoTBDS 2019: 125-136
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