Dear Colleagues,
I am pleased to invite you to my upcoming thesis defense titled "Enhancing Detection
and Explainability in Cybersecurity: A Shift from Machine Learning to Graph
Learning." The event is scheduled for Wednesday, 24 January, at 9:00 AM, at the CRBS
auditorium, 1 rue Eugène Boeckel, 67000 Strasbourg. This is a prospective date conditioned
to the positive evaluation of thesis reviewers.
Jury Members:
Rapporteurs: Mr. Hervé Debar (Prof., Télécom Sud Paris, Evry) and Ms Valeria Loscri
(Prof., INRIA, Lille)
Examiners: Mr. Marc-Oliver Pahl (Prof., IMT Atlantique, Rennes), Ms. Anne Jeannin-Girardin
(McF HDR, ICube, Strasbourg), Mr. Martin Husak (Dr., Masaryk University, Czech Republic),
Ms. Cristel Pelsser (Prof., UCLouvain, Belgium)
Supervisors: Mr. PARREND Pierre (Prof., EPITA Strasbourg, ICube, Strasbourg), Mme DERUYVER
Aline (McF HDR, Université de Strasbourg, ICube, Strasbourg), and Mr. Amhaz Rabih (Dr.,
ICAM-Strasbourg-Europe, ICube, Strasbourg)
Summary: This thesis focuses on transitioning from traditional machine learning to graph
learning in cybersecurity, a crucial shift for representing complex data connections in
cyberattacks. However, a key challenge lies in making the graph learning process
explainable for cyberattack detection, which is critical for explaining the reason behind
the detection and building trust among security analysts. To address this, the research
contributes in three main areas. First, it introduces the ComGLSec workflow, a
benchmarking process to evaluate graph learning methods based on their performance and
inductivity in detecting cyberattacks. Second, it addresses the explainability challenges
in graph learning by developing the Inductive GNNExplainer model for topological
elucidation of attack detections and the GraphSecLearn library to enhance the
contextuality in graph representations of cyberattack patterns. Lastly, the thesis
proposes the XAIMetrics Framework for assessing explainability in machine learning,
incorporating novel metrics to evaluate various aspects of explainability in the data
analysis chain. This comprehensive approach improves understanding and trust in
cybersecurity analysis and lays a foundational groundwork for extending these metrics to
graph learning, highlighting significant advancements in the field.
Keywords: Graph Learning, Explainability, Cybersecurity, Machine Learning, IoT,
Benchmarking, Connected Attack Graphs, Event Graphs, Explainability Metrics.
To help us prepare adequately, kindly confirm your attendance by completing this short
survey:
https://evento.renater.fr/survey/participation-a-la-soutenance-de-these-ama….
Looking forward to your attendance!
Best Regards,
Amani
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