Neutral Agent-based Adversarial Policy Learning against Deep Reinforcement Learning in Multi-party Open Systems
Fuente:
arXiv
Saved in:
| Main Authors: | Peng, Qizhou, Zheng, Yang, Wen, Yu, Wu, Yanna, Du, Yingying |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems
by: Ma, Oubo, et al.
Published: (2024)
by: Ma, Oubo, et al.
Published: (2024)
Adversarial Attack Based Countermeasures against Deep Learning Side-Channel Attacks
by: Gu, Ruizhe, et al.
Published: (2020)
by: Gu, Ruizhe, et al.
Published: (2020)
A Spatiotemporal Stealthy Backdoor Attack against Cooperative Multi-Agent Deep Reinforcement Learning
by: Yu, Yinbo, et al.
Published: (2024)
by: Yu, Yinbo, et al.
Published: (2024)
A Novel and Practical Universal Adversarial Perturbations against Deep Reinforcement Learning based Intrusion Detection Systems
by: Zhang, H., et al.
Published: (2025)
by: Zhang, H., et al.
Published: (2025)
BLAST: A Stealthy Backdoor Leverage Attack against Cooperative Multi-Agent Deep Reinforcement Learning based Systems
by: Fang, Jing, et al.
Published: (2025)
by: Fang, Jing, et al.
Published: (2025)
Adversarial Attacks on Reinforcement Learning Agents for Command and Control
by: Dabholkar, Ahaan, et al.
Published: (2024)
by: Dabholkar, Ahaan, et al.
Published: (2024)
RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted Behaviors
by: Bai, Fengshuo, et al.
Published: (2024)
by: Bai, Fengshuo, et al.
Published: (2024)
Adapting Under Fire: Multi-Agent Reinforcement Learning for Adversarial Drift in Network Security
by: Rivas, Emilia, et al.
Published: (2025)
by: Rivas, Emilia, et al.
Published: (2025)
Adversarial Inception Backdoor Attacks against Reinforcement Learning
by: Rathbun, Ethan, et al.
Published: (2024)
by: Rathbun, Ethan, et al.
Published: (2024)
Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning
by: Jiang, Zhifeng, et al.
Published: (2024)
by: Jiang, Zhifeng, et al.
Published: (2024)
HawkEye: Statically and Accurately Profiling the Communication Cost of Models in Multi-party Learning
by: Ruan, Wenqiang, et al.
Published: (2025)
by: Ruan, Wenqiang, et al.
Published: (2025)
A Practical Adversarial Attack against Sequence-based Deep Learning Malware Classifiers
by: Tan, Kai, et al.
Published: (2025)
by: Tan, Kai, et al.
Published: (2025)
Safety Interventions against Adversarial Patches in an Open-Source Driver Assistance System
by: Chen, Cheng, et al.
Published: (2025)
by: Chen, Cheng, et al.
Published: (2025)
Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning
by: Domico, Kyle, et al.
Published: (2025)
by: Domico, Kyle, et al.
Published: (2025)
PoC-Adapt: Semantic-Aware Automated Vulnerability Reproduction with LLM Multi-Agents and Reinforcement Learning-Driven Adaptive Policy
by: Duy, Phan The, et al.
Published: (2026)
by: Duy, Phan The, et al.
Published: (2026)
The Communication-Friendly Privacy-Preserving Machine Learning against Malicious Adversaries
by: Lu, Tianpei, et al.
Published: (2024)
by: Lu, Tianpei, et al.
Published: (2024)
MAD-Spear: A Conformity-Driven Prompt Injection Attack on Multi-Agent Debate Systems
by: Cui, Yu, et al.
Published: (2025)
by: Cui, Yu, et al.
Published: (2025)
Don't Trust Your Upstream: Exploiting LLM Multi-Agent System via Topology-Guided Adversarial Propagation
by: Liang, Ruichao, et al.
Published: (2025)
by: Liang, Ruichao, et al.
Published: (2025)
Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning
by: Zhang, Yiyao, et al.
Published: (2026)
by: Zhang, Yiyao, et al.
Published: (2026)
LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection
by: Tian, Jiwei, et al.
Published: (2024)
by: Tian, Jiwei, et al.
Published: (2024)
Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing
by: Gibert, Daniel, et al.
Published: (2023)
by: Gibert, Daniel, et al.
Published: (2023)
A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing
by: Gibert, Daniel, et al.
Published: (2024)
by: Gibert, Daniel, et al.
Published: (2024)
SLVC-DIDA: Signature-less Verifiable Credential-based Issuer-hiding and Multi-party Authentication for Decentralized Identity
by: Xie, Tianxiu, et al.
Published: (2025)
by: Xie, Tianxiu, et al.
Published: (2025)
Hierarchical Adversarially-Resilient Multi-Agent Reinforcement Learning for Cyber-Physical Systems Security
by: Alqithami, Saad
Published: (2025)
by: Alqithami, Saad
Published: (2025)
IDCloak: A Practical Secure Multi-party Dataset Join Framework for Vertical Privacy-preserving Machine Learning
by: Chen, Shuyu, et al.
Published: (2025)
by: Chen, Shuyu, et al.
Published: (2025)
M-to-N Backdoor Paradigm: A Multi-Trigger and Multi-Target Attack to Deep Learning Models
by: Hou, Linshan, et al.
Published: (2022)
by: Hou, Linshan, et al.
Published: (2022)
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
by: Lee, Sunwoo, et al.
Published: (2025)
by: Lee, Sunwoo, et al.
Published: (2025)
Bicoptor: Two-round Secure Three-party Non-linear Computation without Preprocessing for Privacy-preserving Machine Learning
by: Zhou, Lijing, et al.
Published: (2022)
by: Zhou, Lijing, et al.
Published: (2022)
Resilient Endurance-Aware NVM-based PUF against Learning-based Attacks
by: Nassar, Hassan, et al.
Published: (2025)
by: Nassar, Hassan, et al.
Published: (2025)
Multi-agent Reinforcement Learning-based Network Intrusion Detection System
by: Tellache, Amine, et al.
Published: (2024)
by: Tellache, Amine, et al.
Published: (2024)
AdaDoS: Adaptive DoS Attack via Deep Adversarial Reinforcement Learning in SDN
by: Shao, Wei, et al.
Published: (2025)
by: Shao, Wei, et al.
Published: (2025)
Client-Cooperative Split Learning
by: Deng, Haiyu, et al.
Published: (2026)
by: Deng, Haiyu, et al.
Published: (2026)
Distributional Black-Box Model Inversion Attack with Multi-Agent Reinforcement Learning
by: Bao, Huan, et al.
Published: (2024)
by: Bao, Huan, et al.
Published: (2024)
Multi-Agent LLM Governance for Safe Two-Timescale Reinforcement Learning in SDN-IoT Defense
by: Jamshidi, Saeid, et al.
Published: (2026)
by: Jamshidi, Saeid, et al.
Published: (2026)
FuzzAgent: Multi-Agent System for Evolutionary Library Fuzzing
by: Lyu, Yunlong, et al.
Published: (2026)
by: Lyu, Yunlong, et al.
Published: (2026)
RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies
by: Behzadan, Vahid, et al.
Published: (2019)
by: Behzadan, Vahid, et al.
Published: (2019)
Trusted Routing for Blockchain-Empowered UAV Networks via Multi-Agent Deep Reinforcement Learning
by: Jia, Ziye, et al.
Published: (2025)
by: Jia, Ziye, et al.
Published: (2025)
MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inference
by: Wang, Yongqin, et al.
Published: (2022)
by: Wang, Yongqin, et al.
Published: (2022)
Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attacks
by: Zheng, Lele, et al.
Published: (2024)
by: Zheng, Lele, et al.
Published: (2024)
Privacy-R1: Privacy-Aware Multi-LLM Agent Collaboration via Reinforcement Learning
by: Hui, Zheng, et al.
Published: (2025)
by: Hui, Zheng, et al.
Published: (2025)
Similar Items
-
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems
by: Ma, Oubo, et al.
Published: (2024) -
Adversarial Attack Based Countermeasures against Deep Learning Side-Channel Attacks
by: Gu, Ruizhe, et al.
Published: (2020) -
A Spatiotemporal Stealthy Backdoor Attack against Cooperative Multi-Agent Deep Reinforcement Learning
by: Yu, Yinbo, et al.
Published: (2024) -
A Novel and Practical Universal Adversarial Perturbations against Deep Reinforcement Learning based Intrusion Detection Systems
by: Zhang, H., et al.
Published: (2025) -
BLAST: A Stealthy Backdoor Leverage Attack against Cooperative Multi-Agent Deep Reinforcement Learning based Systems
by: Fang, Jing, et al.
Published: (2025)