In-depth Comparative Evaluation of Supervised Machine Learning Approaches for Detection of Cybersecurity Threats. - Laurens D'hooge; Tim Wauters; Bruno Volckaert; Filip De Turck (IoTBDS, 2019)

Classification Hardness for Supervised Learners on 20 Years of Intrusion Detection Data. - Laurens D'hooge; Tim Wauters; Bruno Volckaert; Filip De Turck (IEEE Access, 2019)

Unsupervised Machine Learning Techniques for Network Intrusion Detection on Modern Data. - Miel Verkerken; Laurens D'hooge; Tim Wauters; Bruno Volckaert; Filip De Turck (CSNet, 2020)

Inter-dataset generalization strength of supervised machine learning methods for intrusion detection. - Laurens D'hooge; Tim Wauters; Bruno Volckaert; Filip De Turck (J. Inf. Secur. Appl., 2020)

Machine learning based intrusion detection as a service: task assignment and capacity allocation in a multi-tier architecture. - Yuan-Cheng Lai; Didik Sudyana; Ying-Dar Lin; Miel Verkerken; Laurens D'hooge; Tim Wauters; Bruno Volckaert; Filip De Turck (UCC Companion, 2021)

Hierarchical feature block ranking for data-efficient intrusion detection modeling. - Laurens D'hooge; Miel Verkerken; Tim Wauters; Bruno Volckaert; Filip De Turck (Comput. Networks, 2021)

Establishing the Contaminating Effect of Metadata Feature Inclusion in Machine-Learned Network Intrusion Detection Models. - Laurens D'hooge; Miel Verkerken; Bruno Volckaert; Tim Wauters; Filip De Turck (DIMVA, 2022)

Discovering Non-Metadata Contaminant Features in Intrusion Detection Datasets. - Laurens D'hooge; Miel Verkerken; Tim Wauters; Bruno Volckaert; Filip De Turck (PST, 2022)

Towards Model Generalization for Intrusion Detection: Unsupervised Machine Learning Techniques. - Miel Verkerken; Laurens D'hooge; Tim Wauters; Bruno Volckaert; Filip De Turck (J. Netw. Syst. Manag., 2022)

Performance Impact of Queue Sorting in Container-Based Application Scheduling. - José Santos 001; Miel Verkerken; Laurens D'hooge; Tim Wauters; Bruno Volckaert; Filip De Turck (CNSM, 2023)

Castles Built on Sand: Observations from Classifying Academic Cybersecurity Datasets with Minimalist Methods. - Laurens D'hooge; Miel Verkerken; Tim Wauters; Filip De Turck; Bruno Volckaert (IoTBDS, 2023)

Investigating Generalized Performance of Data-Constrained Supervised Machine Learning Models on Novel, Related Samples in Intrusion Detection. - Laurens D'hooge; Miel Verkerken; Tim Wauters; Filip De Turck; Bruno Volckaert (Sensors, 2023)

Task Assignment and Capacity Allocation for ML-Based Intrusion Detection as a Service in a Multi-Tier Architecture. - Yuan-Cheng Lai; Didik Sudyana; Ying-Dar Lin; Miel Verkerken; Laurens D'hooge; Tim Wauters; Bruno Volckaert; Filip De Turck (IEEE Trans. Netw. Serv. Manag., 2023)

Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection. - Laurens D'hooge; Miel Verkerken; Tim Wauters; Filip De Turck; Bruno Volckaert (J. Cybersecur. Priv., 2023)

A Novel Multi-Stage Approach for Hierarchical Intrusion Detection. - Miel Verkerken; Laurens D'hooge; Didik Sudyana; Ying-Dar Lin; Tim Wauters; Bruno Volckaert; Filip De Turck (IEEE Trans. Netw. Serv. Manag., 2023)

ChronosGuards: A Hierarchical Machine Learning Intrusion Detection System for Modern Clouds. - Miel Verkerken; José Santos 001; Laurens D'hooge; Tim Wauters; Bruno Volckaert; Filip De Turck (CNSM, 2024)

RustiFlow: Bridging the Gap Between Security Research and Practice Using eBPF-Based Network Flow Extraction. - Miel Verkerken; Matisse Callewaert; Laurens D'hooge; Tim Wauters; Bruno Volckaert; Filip De Turck (EuroS&P (Workshops), 2025)