- ORCID iD
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0000-0001-5086-6361
- Bio (via ORCID)
- This is the ORCID profile of Laurens D'hooge, a PhD candidate working at Ghent university at the Internet and Data Science Lab (IDLab), in collaboration with Imec. I have primarily worked on (network) intrusion detection, but am generally interested in cybersecurity and the application of data science methods within the field. I work in open source as much as I can, these days primarily as StrGenIx on Kaggle (https://www.kaggle.com/dhoogla). If you need a clean version of an academic security dataset, odds are high that I have published one. You can follow my updates on ResearchGate: https://www.researchgate.net/profile/Laurens-Dhooge
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- Conference Paper
- open access
Performance impact of queue sorting in container-based application Scheduling
(2023) p.1-9 -
Discovery and characterization of flaws in machine-learned network intrusion detection systems and data sets
(2023) -
- Journal Article
- A2
- open access
Characterizing the impact of data-damaged models on generalization strength in intrusion detection
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- Conference Paper
- P1
- open access
Castles built on sand : observations from classifying academic cybersecurity datasets with minimalist methods
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- Journal Article
- A1
- open access
Investigating generalized performance of data-constrained supervised machine learning models on novel, related samples in intrusion detection
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- Journal Article
- A1
- open access
Task assignment and capacity allocation for ML-based intrusion detection as a service in a multi-tier architecture
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- Conference Paper
- C3
- open access
Fixing the foundations : towards generalization for machine learning
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The importance of establishing baselines in ML classification tasks
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Establishing the contaminating effect of metadata feature inclusion in machine-learned network intrusion detection models
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- Conference Paper
- P1
- open access
Discovering non-metadata contaminant features in intrusion detection datasets