Description
Machine learning based detection models can strengthen detection, but there remain some significant barriers to the widespread deployment of such techniques in operational detection systems. In this presentation, we identify the main challenges to overcome and we provide both methodological guidance and practical solutions to address them. The solutions we present are completely generic to be beneficial to any detection problem on any data type and are freely available in SecuML.The content of the presentation is mostly based on my PhD thesis “Expert-in-the-Loop Supervised Learning for Computer Security Detection Systems”.
Practical infos
Next sessions
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Un protocole SMPC de curation de données d'entrainement et sa fragilité aux hypothèses de sécurité...
Speaker : Marc-Olivier Killijian - Université du Québec à Montréal
... ou "Sécurité et insécurité - dans quel état j’erre, ai-je bien rangé mon modèle de sécurité ?" De nos jours, les sources de données, et leurs curateurs, sont répartis à travers le monde. Il arrive que les propriétaires de ces données souhaitent collaborer entre eux afin d’augmenter la qualité de ces données, particulièrement avant d’entrainer des modèles d’apprentissage machine.Dans cet exposé[…]-
SoSysec
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Privacy
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Machine learning
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Distributed systems
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Safety-Security Convergence of Industrial Control Systems
Speaker : Maxime Puys - Université Clermont Auvergne - IUT de Clermont-Ferrand
Industrial Control Systems (ICS) are designed to provide a service, such as power generation or water treatment, while protecting people, assets, and the environment against hazards. However, ICS now integrate Information Technology (IT) and are interconnected with the outside world such as the Internet, thereby exposing their infrastructures to cyberattacks. Cyberattacks have thus become new[…]-
SoSysec
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Intrusion detection
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