TY - GEN
T1 - Deep Reinforcement Learning for Dynamic Network Slice Security Using Moving Target Defense
AU - Andreas, Andreou
AU - Mavromoustakis, Constandinos X.
AU - Song, Houbing
AU - Markakis, Evangelos
AU - Bourdena, Athina
AU - Mastorakis, George
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Network slicing has emerged as a transformative enabler for meeting the diverse requirements of 5G and beyond networks, including 6G. However, network slices' dynamic and virtualized nature introduces significant security challenges, particularly against evolving cyber threats. We propose a Deep Reinforcement Learning (DRL)-based Moving Target Defense (MTD) strategy tailored for secure network slicing to address these challenges. Our approach utilizes a Q-Learning framework to manage MTD actions dynamically, optimizing security while maintaining service quality. Extensive simulations demonstrate the effectiveness of our framework in minimizing attack success rates and ensuring operational stability, significantly outperforming baseline methods such as random decision-making.
AB - Network slicing has emerged as a transformative enabler for meeting the diverse requirements of 5G and beyond networks, including 6G. However, network slices' dynamic and virtualized nature introduces significant security challenges, particularly against evolving cyber threats. We propose a Deep Reinforcement Learning (DRL)-based Moving Target Defense (MTD) strategy tailored for secure network slicing to address these challenges. Our approach utilizes a Q-Learning framework to manage MTD actions dynamically, optimizing security while maintaining service quality. Extensive simulations demonstrate the effectiveness of our framework in minimizing attack success rates and ensuring operational stability, significantly outperforming baseline methods such as random decision-making.
KW - 6G Networks
KW - Cybersecurity
KW - Deep Reinforcement Learning (DRL)
KW - Moving Target Defense (MTD)
KW - Network Slicing
KW - Proactive Defense
KW - Q-Learning
UR - https://www.scopus.com/pages/publications/105011359239
U2 - 10.1109/IWCMC65282.2025.11059587
DO - 10.1109/IWCMC65282.2025.11059587
M3 - Conference contribution
AN - SCOPUS:105011359239
T3 - 21st International Wireless Communications and Mobile Computing Conference, IWCMC 2025
SP - 398
EP - 403
BT - 21st International Wireless Communications and Mobile Computing Conference, IWCMC 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2025
Y2 - 12 May 2024 through 16 May 2024
ER -