Presentation | 2020-12-11 A Preliminary Multi-Agent Reinforcement Learning Approach for Responding Dynamic Traffic in Communication Destination Anonymization Keita Sugiyama, Naoki Fukuta, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this paper, we describe our prototype mechanism using the simulation-based multi-agent reinforcement learning for automatically allocating resources for anonymizing communication destinations as one of the applications of the multi-agent techniques for network virtualization. As an example of concrete scenarios, we assume a network where end-hosts are connected frequently and traffic trends change frequently for this reason. In this scenario, we implement a mechanism that allows multiple agents represented by network switches to cooperate with other agents autonomously for adjusting the level of anonymity using multi-agent reinforcement learning, and confirm the effect by simulation. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Multi-Agent Reinforcement Learning / Moving Target Defense / Network Security |
Paper # | AI2020-10 |
Date of Issue | 2020-12-03 (AI) |
Conference Information | |
Committee | AI |
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Conference Date | 2020/12/10(1days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | Online and HAMAMATSU ACT CITY |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | Foundations and application technologies for AI systems on the new normal |
Chair | Naoki Fukuta(Shizuoka Univ.) |
Vice Chair | Yuichi Sei(Univ. of Electro-Comm.) / Yuko Sakurai(AIST) |
Secretary | Yuichi Sei(Nagoya Inst. of Tech.) / Yuko Sakurai(Tokyo Univ. of Agriculture and Technology) |
Assistant |
Paper Information | |
Registration To | Technical Committee on Artificial Intelligence and Knowledge-Based Processing |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | A Preliminary Multi-Agent Reinforcement Learning Approach for Responding Dynamic Traffic in Communication Destination Anonymization |
Sub Title (in English) | |
Keyword(1) | Multi-Agent Reinforcement Learning |
Keyword(2) | Moving Target Defense |
Keyword(3) | Network Security |
1st Author's Name | Keita Sugiyama |
1st Author's Affiliation | Shizuoka University(Shizuoka Univ.) |
2nd Author's Name | Naoki Fukuta |
2nd Author's Affiliation | Shizuoka University(Shizuoka Univ.) |
Date | 2020-12-11 |
Paper # | AI2020-10 |
Volume (vol) | vol.120 |
Number (no) | AI-281 |
Page | pp.pp.46-51(AI), |
#Pages | 6 |
Date of Issue | 2020-12-03 (AI) |