Defending Network Traffic Attack with Distributed Multi-agent Reinforcement Learning

Xia, Shiming; Bai, Wei; Zhou, Xingyu; Pan, Zhisong; Guo, Shize

doi:10.1007/978-981-32-9298-7_17

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…The authors in [180] and [181] propose to adopt MARL framework for the purpose of collaborative DDoS defending. The system model involves hosts, routers, and servers.…”

Section: Ddos Attackmentioning

confidence: 99%

“…To overcome the issues of CRLRT, the authors in [181] introduce an MADDPG-like decentralized ComDDPG method to train agents. Different from MADDPG, the ComDDPG consists of multiple decentralized actors for action execution and one central critic for the centralized training.…”

Section: Ddos Attackmentioning

confidence: 99%

“…3) Malicious packet knowledge: In security solutions, such as [181] and [180], MARL is used to enable each router to learn an collaborative traffic control policy that aims to maximize the average legitimate traffic rate of all the routers. For this, the rewards in these solutions are defined based on the assumption of the knowledge of malicious packets.…”

Section: ) Synchronization Of Multiple Agents In Marlmentioning

confidence: 99%

See 2 more Smart Citations

Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey

Li¹,

Zhu²,

Luong³

et al. 2021

Preprint

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Future Internet involves several emerging technologies such as 5G and beyond 5G networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of Things (IoTs). Moreover, future Internet becomes heterogeneous and decentralized with a large number of involved network entities. Each entity may need to make its local decision to improve the network performance under dynamic and uncertain network environments. Standard learning algorithms such as single-agent Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) have been recently used to enable each network entity as an agent to learn an optimal decision-making policy adaptively through interacting with the unknown environments. However, such an algorithm fails to model the cooperations or competitions among network entities, and simply treats other entities as a part of the environment that may result in the non-stationarity issue. Multi-agent Reinforcement Learning (MARL) allows each network entity to learn its optimal policy by observing not only the environments, but also other entities' policies. As a result, MARL can significantly improve the learning efficiency of the network entities, and it has been recently used to solve various issues in the emerging networks. In this paper, we thus review the applications of MARL in the emerging networks. In particular, we provide a tutorial of MARL and a comprehensive survey of applications of MARL in next generation Internet. In particular, we first introduce single-agent RL and MARL. Then, we review a number of applications of MARL to solve emerging issues in future Internet. The issues consist of network access, transmit power control, computation offloading, content caching, packet routing, trajectory design for UAV-aided networks, and network security issues. Finally, we discuss the challenges, open issues, and future directions related to the applications of MARL in future Internet.

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“…The authors in [180] and [181] propose to adopt MARL framework for the purpose of collaborative DDoS defending. The system model involves hosts, routers, and servers.…”

Section: Ddos Attackmentioning

confidence: 99%

Section: Ddos Attackmentioning

confidence: 99%