How to Disturb Network Reconnaissance: A Moving Target Defense Approach Based on Deep Reinforcement Learning-Reference-Cited by-同舟云学术

How to Disturb Network Reconnaissance: A Moving Target Defense Approach Based on Deep Reinforcement Learning

Published:2023 Issue: Volume:18 Page:5735-5748
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Zhang Tao¹^ORCID,Xu Changqiao²^ORCID,Shen Jiahao³^ORCID,Kuang Xiaohui⁴,Grieco Luigi Alfredo⁵^ORCID

Affiliation:

1. School of Software Engineering, Beijing Jiaotong University, Beijing, China

2. State Key Laboratory of Networking and Switching Technology, Beijing, China

3. School of Computer Science and Engineering, Beihang University, Beijing, China

4. National Key Laboratory of Science and Technology on Information System Security, Beijing, China

5. Department of Electrical and Information Engineering and the Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), Politecnico di Bari, Bari, Italy

Funder

National Natural Science Foundation of China

Advanced Research Program in the 14th Five-Year Plan

Italian MIUR PRIN

Italian MIUR PON Projects AGREED

CHINA RAILWAY Science and Technology Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

http://xplorestaging.ieee.org/ielx7/10206/9970396/10247015.pdf?arnumber=10247015

Reference63 articles.

1. FRVM: Flexible Random Virtual IP Multiplexing in Software-Defined Networks

2. DESIR: Decoy-enhanced seamless IP randomization

3. Security and Performance Assessment of IP Multiplexing Moving Target Defence in Software Defined Networks

4. SDN-Based IP Shuffling Moving Target Defense with Multiple SDN Controllers

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