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Duration: 30 min
Type: Talk
Speakers: Mathieu Fourcroy, Pauline Bourmeau (Cookie), William Robinet
Abstract
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.
Description
This talk covers the architecture of the project, challenges we encountered and design choices we made during development.
This opensource project was co-financed by the LU-CID initiative by the Ministry of Economy Luxembourg.