DIVERGENCE: Deep Reinforcement Learning-Based Adaptive Traffic Inspection and Moving Target Defense Countermeasure Framework-Reference-Cited by-同舟云学术

DIVERGENCE: Deep Reinforcement Learning-Based Adaptive Traffic Inspection and Moving Target Defense Countermeasure Framework

Author:

Kim Sunghwan¹^ORCID,Yoon Seunghyun²^ORCID,Cho Jin-Hee³^ORCID,Kim Dong Seong⁴^ORCID,Moore Terrence J.⁵^ORCID,Free-Nelson Frederica⁵,Lim Hyuk⁶^ORCID

Affiliation:

1. Samsung Research, Samsung Electronics, Seoul, South Korea

2. Korea National Engineering Technology Center, Korea Institute of Industrial Technology, Cheonan, South Korea

3. Department of Computer Science, Virginia Tech, Falls Church, VA, USA

4. School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia

5. Network Science Division, U.S. Army Research Laboratory, Adelphi, MD, USA

6. AI Graduate School, Gwangju Institute of Science and Technology, Gwangju, South Korea

Funder

International Technology Center Pacific

Army Research Office

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

Reference54 articles.

5. OpenFlow based load balancing for fat-tree networks with multipath support;li;Proc Int Conf Commun (ICC),2013

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