Adversarial Attacks Against Reinforcement Learning Based Tactical Networks: A Case Study
Author:
Affiliation:
1. Communication Systems (KOM), Fraunhofer FKIE,Bonn,Germany
2. Secure Communications & Information (SIX), Thales Deutschland,Ditzingen,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10017432/10017302/10017788.pdf?arnumber=10017788
Reference17 articles.
1. TNT: A Tactical Network Test Platform to Evaluate Military Systems Over Ever-Changing Scenarios
2. An Intelligent Model to Quantify the Robustness of Tactical Systems to Unplanned Link Disconnections
3. Maximizing the Probability of Message Delivery Over Ever-Changing Communication Scenarios in Tactical Networks
4. Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning
5. Robust deep reinforcement learning with adversarial attacks;pattanaik;ArXiv,2017
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2. Cooperative Agent System for Quantifying Link Robustness in Tactical Networks;MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM);2023-10-30
3. Mobility Prediction at the Tactical Edge: A Handover for Centralized/Decentralized Networks;2023 International Conference on Military Communications and Information Systems (ICMCIS);2023-05-16
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