Reinforcement Learning for Dynamic Security Policy Enforcement in Communication Networks
DOI:
https://doi.org/10.48047/g3rqkk97Keywords:
Reinforcement Learning, Dynamic Security Policy, Software-Defined Networking, Intrusion Detection, CybersecurityAbstract
In today's modern communication networks, it is imperative to employ dynamic and intelligent mechanisms of security policy enforcement as the complexity of the cyber threats increases. A study of the application of RL algorithms to improve adaptive security policies in Software Defined Networking (SDN) and IoT architecture is provided by this research.To detect real time threats, adjust policy, and change access control, we implemented four combinations of Reinforcement Learning algorithms such as Q learning, Deep Q networks (DQN), Soft Actor critic (SAC), and Multi agent Deep Deterministic Policy Gradient (MADDPG).
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References
ABBAS SHAH, S.F., MAZHAR, T., SHLOUL, T.A., SHAHZAD, T., YU-CHEN, H., MALLEK, F. and HAMAM, H., 2024. Applications, challenges, and solutions of unmanned aerial vehicles in smart city using blockchain. PeerJ Computer Science, .
ABDEL HAKEEM, S.,A., HUSSEIN, H.H. and KIM, H., 2022. Security Requirements and Challenges of 6G Technologies and Applications. Sensors, 22(5), pp. 1969.
ABDOLLAHI, A., ARZANDEH, S.B. and SHEIBANI, M., 2024. Privacy and safety of narrowband internet of things devices. Telkomnika, 22(4), pp. 969-975.
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