Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies
In this paper, we present a multi-agent deep reinforcement learning (deep RL) framework for network slicing in a dynamic environment with multiple base stations and multiple users. In particular, we propose a novel deep RL framework with multiple actors and centralized critic (MACC) in which actors...
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| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| Online Access: | https://ieeexplore.ieee.org/document/10322663/ |
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| author | Feng Wang M. Cenk Gursoy Senem Velipasalar |
| author_facet | Feng Wang M. Cenk Gursoy Senem Velipasalar |
| author_sort | Feng Wang |
| collection | DOAJ |
| description | In this paper, we present a multi-agent deep reinforcement learning (deep RL) framework for network slicing in a dynamic environment with multiple base stations and multiple users. In particular, we propose a novel deep RL framework with multiple actors and centralized critic (MACC) in which actors are implemented as pointer networks to fit the varying dimension of input. We evaluate the performance of the proposed deep RL algorithm via simulations to demonstrate its effectiveness. Subsequently, we develop a deep RL based jammer with limited prior information and limited power budget. The goal of the jammer is to minimize the transmission rates achieved with network slicing and thus degrade the network slicing agents’ performance. We design a jammer with both listening and jamming phases and address jamming location optimization as well as jamming channel optimization via deep RL. We evaluate the jammer at the optimized location, generating interference attacks in the optimized set of channels by switching between the jamming phase and listening phase. We show that the proposed jammer can significantly reduce the victims’ performance without direct feedback or prior knowledge on the network slicing policies. Finally, we devise a Nash-equilibrium-supervised policy ensemble mixed strategy profile for network slicing (as a defensive measure) and jamming. We evaluate the performance of the proposed policy ensemble algorithm by applying on the network slicing agents and the jammer agent in simulations to show its effectiveness. |
| format | Article |
| id | doaj-art-d29ea95130ae4995a3fcfd7e9d8a58ad |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-d29ea95130ae4995a3fcfd7e9d8a58ad2025-08-20T02:57:19ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2024-01-012496310.1109/TMLCN.2023.333423610322663Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive StrategiesFeng Wang0https://orcid.org/0000-0001-8071-9995M. Cenk Gursoy1https://orcid.org/0000-0002-7352-1013Senem Velipasalar2https://orcid.org/0000-0002-1430-1555Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USADepartment of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USADepartment of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USAIn this paper, we present a multi-agent deep reinforcement learning (deep RL) framework for network slicing in a dynamic environment with multiple base stations and multiple users. In particular, we propose a novel deep RL framework with multiple actors and centralized critic (MACC) in which actors are implemented as pointer networks to fit the varying dimension of input. We evaluate the performance of the proposed deep RL algorithm via simulations to demonstrate its effectiveness. Subsequently, we develop a deep RL based jammer with limited prior information and limited power budget. The goal of the jammer is to minimize the transmission rates achieved with network slicing and thus degrade the network slicing agents’ performance. We design a jammer with both listening and jamming phases and address jamming location optimization as well as jamming channel optimization via deep RL. We evaluate the jammer at the optimized location, generating interference attacks in the optimized set of channels by switching between the jamming phase and listening phase. We show that the proposed jammer can significantly reduce the victims’ performance without direct feedback or prior knowledge on the network slicing policies. Finally, we devise a Nash-equilibrium-supervised policy ensemble mixed strategy profile for network slicing (as a defensive measure) and jamming. We evaluate the performance of the proposed policy ensemble algorithm by applying on the network slicing agents and the jammer agent in simulations to show its effectiveness.https://ieeexplore.ieee.org/document/10322663/Network slicingdynamic channel accessdeep reinforcement learningmulti-agent actor-criticadversarial learningpolicy ensemble |
| spellingShingle | Feng Wang M. Cenk Gursoy Senem Velipasalar Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies IEEE Transactions on Machine Learning in Communications and Networking Network slicing dynamic channel access deep reinforcement learning multi-agent actor-critic adversarial learning policy ensemble |
| title | Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies |
| title_full | Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies |
| title_fullStr | Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies |
| title_full_unstemmed | Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies |
| title_short | Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies |
| title_sort | robust network slicing multi agent policies adversarial attacks and defensive strategies |
| topic | Network slicing dynamic channel access deep reinforcement learning multi-agent actor-critic adversarial learning policy ensemble |
| url | https://ieeexplore.ieee.org/document/10322663/ |
| work_keys_str_mv | AT fengwang robustnetworkslicingmultiagentpoliciesadversarialattacksanddefensivestrategies AT mcenkgursoy robustnetworkslicingmultiagentpoliciesadversarialattacksanddefensivestrategies AT senemvelipasalar robustnetworkslicingmultiagentpoliciesadversarialattacksanddefensivestrategies |