16 Mar
2024
16 Mar
'24
2:50 p.m.
Bonjour à tous,
Vous trouverez ci-dessous l'appel à article pour la session 'Detection of complex
attacks' que nous organisons lors de la conférence KES'24.
N'hésitez pas à transmettre à vos partenaires ! (les partenaires habituels de
l'équipe sont déjà informés).
Bien à vous,
Pierre
Dear partners,
Please find below the call for paper for the Session on 'Detection of Complex
Attacks' at KES'2024.
Session: 'Detection of Complex Attacks through Advanced Learning Models'
Conference: KES'2024, the 28th International Conference on Knowledge-Based and
Intelligent Information & Engineering Systems
When: 11-13 September 2024
Where: Seville, Spain
Deadline: 8th April 2024
All information: https://kes-dca.lre.epita.fr/
Submission: http://kes2024.kesinternational.org/easychair.php
Scope of Session
This session intends to address the next challenges in the coupling of cybersecurity and
AI by focusing on a blind spot of detection of complex cybersecurity attacks: the analysis
of weak signals and stealthy interactions inside the systems to be protected.
Attacks and their countermeasures have grown dramatically more complex with the
combination of extensive digital transformation in service and industries, the maturation
of both defense and attack software, and the growing pressure of increasing cybersecurity
threats. In this context, efficient detection requires a radical refinement of these
systems which can no longer be considered as monolithic (or monolithic abstractions). The
specificities of the user, machine, operating system, and service levels must be
considered, while maintaining a technical control, and a cognitive one for the operator in
charge, over the ever-growing heterogeneity. In particular: weak signals, traffic
betraying an ongoing APT (advanced Persistent Threat), or attacks against the detection
systems easily evade state of the art detectors. Being able to hunt these novel threats
necessitates to support the identification of emerging behaviors, tracking the evolution
of connections as well as connection patterns, or even making correlations through remote
systems. And to do so in an antagonist environment where the adversary does not passively
wait to be detected but takes actives steps to evade, lure or exploit the detection
systems.
The session on "Interactions for security detection" deals with following key
challenges:
* How to model interactions between users, machines, systems, and services?
* How to detect low signals and their drift, as well as learn and handle novel threats
in antagonist environments?
* How to exploit these low signals to abuse operational and protection systems
* How to design robust systems, detection systems (federated learning), or bricks of
detection systems (SOCs at system and user level)
Topics of interest are, but not restricted to:
· Learning emerging behaviors for security detection
· Low signals for detection
· Graph representation learning for security: knowledge, provenance, connectivity
graphs.
· Advanced learning paradigms
· Distributed learning and Decentralized learning
· Federated learning
· Stream learning
· User interactions
· Machine learning for security attack and defense
· Detection in heterogeneous environments
· LLM for security, security for LLMs
· Adversarial machine learning
· Trustworthy machine learning
Application domains are, but not restricted to:
· IoT environments
· Critical infrastructures
· Cloud infrastructures
· IT Networks
Best regards,
Pierre Parrend, Marc-Oliver Pahl, Nidà Meddouri
Pierre Parrend
Deputy Head of LRE - Research Laboratory at EPITA
Head of Security and Systems Team
Professor, Dr. HDR
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