Deep Reinforcement Learning Based Fast Anomaly Detection and Localization for Programmable Networks-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning Based Fast Anomaly Detection and Localization for Programmable Networks

Published:2023-05-28 Issue: Volume: Page:
ISSN:
Container-title:ICC 2023 - IEEE International Conference on Communications
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Author:

Zhan Peng¹,Qin Guangyi¹,Qian Xingxin¹,Wang Xiong¹,Ren Jing¹,Zhuang Zirui²,Xu Shizhong¹

Affiliation:

1. University of Electronic Science and Technology of China,Chengdu,China

2. Beijing University of Posts and Telecommunications,Beijing,China

Funder

National Key R&D Program of China

NSFC Fund

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10278505/10278554/10278843.pdf?arnumber=10278843

Reference20 articles.

1. Sketching the Delay: Tracking Temporally Uncorrelated Flow-Level Latencies;cuxart;ACM SIGCOMM,2011

2. Not All Microseconds are Equal: Fine-Grained Per-Flow Measurements with Reference Latency Interpolation;lee;ACM SIGCOMM,2011

3. Identifiablity of Link Metrics Based on End-to-End Path Measurements;ma;ACM IMC,2013

4. On Identifying Additive Link Metrics Using Linearly Independent Cycles and Paths

5. Loss Radar: Fast Detection of Lost Packets in Data Center Networks;li;ACM CoNeXT,2016

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DRL-Tomo: a deep reinforcement learning-based approach to augmented data generation for network tomography;The Computer Journal;2024-07-21

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