TY - GEN
T1 - Advantages of Deep Reinforcement Learning in Moving Target Defense for Network Slicing Security
AU - Andreou, Andreas
AU - Mavromoustakis, Constandinos X.
AU - Markakis, Evangelos
AU - Bourdena, Athina
AU - Mastorakis, George
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Network slicing networks is crucial for supporting diverse services, requiring stringent security. Traditional AI-based security solutions are reactive, addressing threats post-occurrence. This demo paper proposes integrating Deep Reinforcement Learning (DRL) with MTD to enhance security in network slicing. By dynamically shuffling IP addresses, the network becomes a robust moving target, effectively neutralizing DDoS attacks. Experimental results validate this DRL-MTD framework's effectiveness, paving the way for scalable, proactive defense mechanisms in advanced network architectures.
AB - Network slicing networks is crucial for supporting diverse services, requiring stringent security. Traditional AI-based security solutions are reactive, addressing threats post-occurrence. This demo paper proposes integrating Deep Reinforcement Learning (DRL) with MTD to enhance security in network slicing. By dynamically shuffling IP addresses, the network becomes a robust moving target, effectively neutralizing DDoS attacks. Experimental results validate this DRL-MTD framework's effectiveness, paving the way for scalable, proactive defense mechanisms in advanced network architectures.
KW - Deep Reinforcement Learning
KW - Moving Target Defense
KW - Network Slicing
UR - https://www.scopus.com/pages/publications/105002866515
U2 - 10.1109/CAMAD62243.2024.10942954
DO - 10.1109/CAMAD62243.2024.10942954
M3 - Conference contribution
AN - SCOPUS:105002866515
T3 - IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD
BT - 2024 IEEE 29th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 29th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024
Y2 - 21 October 2024 through 23 October 2024
ER -