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Security and Privacy Issues in Deep Reinforcement Learning: Threats and Countermeasures
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Revealing the Risk of Hyper-Parameter Leakage in Deep Reinforcement Learning Models
inferred← citeshttps://doi.org/10.1109/tetc.2025.3631134openalex-snapshot
IsBad: Imperceptible Sample-Specific Backdoor to DNN With Denoising Autoencoder
inferred← citeshttps://doi.org/10.1109/tifs.2025.3613955openalex-snapshot
Improving Privacy Budget Auditing of Differentially Private Artificial Intelligence Models Through Variance of Model Parameters
inferred← citeshttps://doi.org/10.1109/tits.2025.3647284openalex-snapshot
Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Survey
inferred← citeshttps://doi.org/10.1109/tits.2026.3666151openalex-snapshot
A Systematic Review of Adversarial Attacks and Defenses for Deep Reinforcement Learning in Autonomous Vehicle Applications
inferred← citeshttps://doi.org/10.3389/fcomp.2025.1670238openalex-snapshot
Market malicious bidding user detection based on multi-agent reinforcement learning
inferred← citeshttps://doi.org/10.3390/en17246420openalex-snapshot
Applications of Deep Reinforcement Learning for Home Energy Management Systems: A Review
inferred← citeshttps://doi.org/10.3390/s26154802openalex-snapshot
Distributed Artificial Intelligence for IoT Security: A Structured Review
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.