TY - GEN
T1 - Towards AI-Driven Security in the Edge—Cloud Continuum
T2 - 22nd IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2026
AU - Tomas, Pedro R.
AU - Fernandes, João
AU - Miola, Davide
AU - Chrysos, Grigorios
AU - Kandoi, Rajat
AU - Javadpour, Amir
AU - Sisto, Riccardo
AU - Dippold, Matthias
AU - Kopanaki, Despina
AU - Ioannidis, Sotiris
AU - Maragkou, Sofia
AU - Cordeiro, Luís
AU - Taleb, Tarik
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2027.
PY - 2027
Y1 - 2027
N2 - The evolution towards Beyond 5G (B5G)/6G systems is accelerating the emergence of distributed Edge–Cloud environments, where computation and intelligence span heterogeneous and dynamic infrastructures. While this enables latency-sensitive and data-intensive services, it also expands the attack surface, rendering traditional perimeter-based security insufficient. In this context, Artificial Intelligence (AI)-driven security is emerging as a key approach for enabling adaptive monitoring, intelligent threat detection, and automated response. This paper presents an integration-oriented perspective on AI-driven security in the Edge–Cloud continuum. It identifies the main security requirements and design dimensions, and analyses representative building blocks, including eBPF-based monitoring, hardware-accelerated intrusion detection, federated intelligence, and privacy-preserving mechanisms. Based on these elements, the paper outlines a unified architectural framework that integrates telemetry collection, AI-driven detection, distributed learning, and trusted orchestration into an end-to-end security pipeline. The approach is further supported by insights from the ELASTIC and 6G-PATH projects, highlighting its applicability in realistic deployment scenarios. Finally, the paper discusses key challenges related to scalability, trust, and robustness in next-generation Edge–Cloud systems.
AB - The evolution towards Beyond 5G (B5G)/6G systems is accelerating the emergence of distributed Edge–Cloud environments, where computation and intelligence span heterogeneous and dynamic infrastructures. While this enables latency-sensitive and data-intensive services, it also expands the attack surface, rendering traditional perimeter-based security insufficient. In this context, Artificial Intelligence (AI)-driven security is emerging as a key approach for enabling adaptive monitoring, intelligent threat detection, and automated response. This paper presents an integration-oriented perspective on AI-driven security in the Edge–Cloud continuum. It identifies the main security requirements and design dimensions, and analyses representative building blocks, including eBPF-based monitoring, hardware-accelerated intrusion detection, federated intelligence, and privacy-preserving mechanisms. Based on these elements, the paper outlines a unified architectural framework that integrates telemetry collection, AI-driven detection, distributed learning, and trusted orchestration into an end-to-end security pipeline. The approach is further supported by insights from the ELASTIC and 6G-PATH projects, highlighting its applicability in realistic deployment scenarios. Finally, the paper discusses key challenges related to scalability, trust, and robustness in next-generation Edge–Cloud systems.
KW - 6G
KW - AI-Based Intrusion Detection
KW - AI-Driven Security
KW - eBPF
KW - Edge–Cloud Continuum
KW - Federated Learning
UR - https://www.scopus.com/pages/publications/105045712564
U2 - 10.1007/978-3-032-30507-7_20
DO - 10.1007/978-3-032-30507-7_20
M3 - 会议稿件
AN - SCOPUS:105045712564
SN - 9783032305060
T3 - IFIP Advances in Information and Communication Technology
SP - 301
EP - 315
BT - Artificial Intelligence Applications and Innovations. AIAI 2026 IFIP WG 12.5 International Workshops - B5G-Pine 2026, Proceedings
A2 - Papaleonidas, Antonios
A2 - Pimenidis, Elias
A2 - Chochliouros, Ioannis
A2 - Krinidis, Stelios
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 16 July 2026 through 19 July 2026
ER -