TY - GEN
T1 - Interactive DRL-Orchestrated Security Management in NFV-SDN Networks
AU - Andreas, Andreou
AU - Mavromoustakis, Constandinos X.
AU - Mastorakis, George
AU - Bourdena, Athina
AU - Markakis, Evangelos
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - The demo paper presents a dynamic security orchestration framework for NFV-SDN networks, leveraging Deep Reinforcement Learning (DRL) to proactively mitigate cyber threats. An interactive simulation shows real-time network state, attack response, and performance gains over static security approaches.
AB - The demo paper presents a dynamic security orchestration framework for NFV-SDN networks, leveraging Deep Reinforcement Learning (DRL) to proactively mitigate cyber threats. An interactive simulation shows real-time network state, attack response, and performance gains over static security approaches.
KW - Deep Reinforcement Learning
KW - Dynamic Orchestration
KW - NFV
KW - SDN
KW - Security
UR - https://www.scopus.com/pages/publications/105033359441
U2 - 10.1109/NFV-SDN66355.2025.11349364
DO - 10.1109/NFV-SDN66355.2025.11349364
M3 - Conference contribution
AN - SCOPUS:105033359441
T3 - 2025 IEEE Conference on Network Function Virtualization and Software-Defined Networking, NFV-SDN 2025
BT - 2025 IEEE Conference on Network Function Virtualization and Software-Defined Networking, NFV-SDN 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE Conference on Network Function Virtualization and Software-Defined Networking, NFV-SDN 2025
Y2 - 10 November 2025 through 12 November 2025
ER -