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 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.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep Reinforcement LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery
Next-generation (NextG) cellular networks are designed to support emerging applications with diverse data rate and latency requirements, such as immersive multimedia services and large-scale Internet of Things deployment…
Reinforcement LearningAdversarial attacks in consensus-based multi-agent reinforcement learning
Recently, many cooperative distributed multi-agent reinforcement learning (MARL) algorithms have been proposed in the literature. In this work, we study the effect of adversarial attacks on a network that employs a conse…
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agents
Adversarial examples can be useful for identifying vulnerabilities in AI systems before they are deployed. In reinforcement learning (RL), adversarial policies can be developed by training an adversarial agent to minimiz…
Red Teamingreinforcement-learningReinforcement Learning (RL)Adversarial Attacks on Deep Algorithmic Trading Policies
Deep Reinforcement Learning (DRL) has become an appealing solution to algorithmic trading such as high frequency trading of stocks and cyptocurrencies. However, DRL have been shown to be susceptible to adversarial attack…
Algorithmic TradingDeep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Learning to Cope with Adversarial Attacks
The security of Deep Reinforcement Learning (Deep RL) algorithms deployed in real life applications are of a primary concern. In particular, the robustness of RL agents in cyber-physical systems against adversarial attac…
Decision MakingDeep Reinforcement LearningMeta-LearningReinforcement Learning