Pauline Bourmeau (Cookie)
Pauline Bourmeau is an independent security researcher specializing in the intersection of artificial intelligence, cognitive psychology, and threat intelligence. She has consulted on multilingual natural language processing, led deep learning and NLP workshops, and created training materials blending STEM with human factors. As founder of DEFCON Paris and contributor to the MISP project, she actively advances collaborative cybersecurity practices.
Previously, Pauline worked as a Threat Intelligence Analyst conducting OSINT, HUMINT, and SOCINT analysis to profile threats and investigate APTs. She holds a Master’s in Criminology with a thesis on cybersecurity intelligence sharing, and a background in sociolinguistics and computer science from Sorbonne and School 42.
Sessions
This session provides cybersecurity professionals with practical machine learning skills, from ML basics up to deep learning with TensorFlow. Participants will set up a complete development environment and learn foundational ML concepts through hands-on implementation rather than mathematical theory. The curriculum covers core ML principles through deep learning, with emphasis on security-relevant applications. No advanced mathematics or prior AI experience required.
We break the myth. You don't need a PhD to do AI here.
This session provides cybersecurity professionals with practical machine learning skills, from ML basics up to deep learning with TensorFlow. Participants will set up a complete development environment and learn foundational ML concepts through hands-on implementation rather than mathematical theory. The curriculum covers core ML principles through deep learning, with emphasis on security-relevant applications. No advanced mathematics or prior AI experience required.
We break the myth. You don't need a PhD to do AI here.
Nowadays, the detection of generic mass-scale phishing attacks is quite
effective. Techniques that leverage indicators of compromise (IOCs) collection
and sharing tools, such as MISP (the Open Source Threat Intelligence Sharing
Platform), are well established and give good results in the field. However,
detection of targeted attack attempts aka spear-phishing, is much more
challenging because the attackers exploit contextual information about the
targets they aim for.
By using up-to-date, relevant and precise information about the inner
operations of the targeted company, attackers can make their deception far more
effective.
SPOT makes use of state-of-the-art natural language
processing (NLP) techniques based on machine learning (ML) and large language
models (LLMs) in particular to try to detect and prevent spear-phishing
attack attempts.
This opensource project was co-financed by the LU-CID initiative by the Ministry
of Economy Luxembourg.