[en] A large body of research has been accomplished on
prevention and detection of malicious events, attacks, threats, or
botnets. However, there is a lack of automatic and sophisticated
methods for investigating malicious events/users, understanding
the root cause of attacks, and discovering what is really hap-
pening before an attack. In this paper, we propose an attack
model discovery approach for investigating and mining malicious
authentication events across user accounts. The approach is based
on process mining techniques on event logs reaching attacks in
order to extract the behavior of malicious users. The evaluation
is performed on a publicly large dataset, where we extract models
of the behavior of malicious users via authentication events. The
results are useful for security experts in order to improve defense
tools by making them robust and develop attack simulations.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
LAGRAA, Sofiane ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
STATE, Radu ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Process mining-based approach for investigating malicious login events
Date de publication/diffusion :
2020
Nom de la manifestation :
IEEE/IFIP Network Operations and Management Symposium (NOMS)
Date de la manifestation :
20-24 April 2020
Manifestation à portée :
International
Titre de l'ouvrage principal :
IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary, April 20-24, 2020
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