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Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-Based Traffic Congestion Control Systems

  • Published:2021 Issue: Volume:16 Page:4772-4787
  • ISSN:1556-6013
  • Container-title:IEEE Transactions on Information Forensics and Security
  • language:
  • Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Wang YueORCID,Sarkar EshaORCID,Li WenqingORCID,Maniatakos MichailORCID,Jabari Saif EddinORCID

Funder

New York University Abu Dhabi (NYUAD) Center for Interacting Urban Networks (CITIES) by Tamkeen under NYUAD Research Institute

Swiss Re Institute under the Quantum Cities initiative

Center for CyberSecurity (CCS) by Tamkeen under NYUAD Research Institute

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Cited by 19 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. Backdozer : A Backdoor Detection Methodology for DRL-based Traffic Controllers;ACM Journal on Autonomous Transportation Systems;2024-01-08

2. MBA: Backdoor Attacks Against 3D Mesh Classifier;IEEE Transactions on Information Forensics and Security;2024

3. Physical Backdoor Trigger Activation of Autonomous Vehicle Using Reachability Analysis;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13

4. Multi-vehicle Platoon Overtaking Using NoisyNet Multi-agent Deep Q-Learning Network;Communications in Computer and Information Science;2023-11-26

5. On the Vulnerability of Deep Reinforcement Learning to Backdoor Attacks in Autonomous Vehicles;Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing;2023-10-07
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