Nash Equilibrium Between Consumer Electronic Devices and DoS Attacker for Distributed IoT-enabled RSE Systems

2025-04-13

AI Summary

To address the dual challenges of insufficient robustness in remote state estimation (RSE) for IoT consumer electronics under denial-of-service (DoS) attacks and stringent edge-resource constraints, this paper models the interaction between devices and attackers as a zero-sum game and proposes a distributed joint estimation framework based on Minimax-DQN. It is the first work to introduce Minimax-DQN into adversarial RSE settings, replacing conventional Q-tables with Q-networks and compressing the action space to accelerate Nash equilibrium (NE) convergence. Two architectures—centralized and distributed—are designed, integrating Kalman filtering with open-loop and closed-loop DoS attack models. Experiments demonstrate that the proposed method significantly improves both the recovery speed and stability of the RSE error covariance, while achieving superior NE convergence efficiency and estimation robustness compared to state-of-the-art approaches.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Adversarial Learning & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements

Abstract

In electronic consumer Internet of Things (IoT), consumer electronic devices as edge devices require less computational overhead and the remote state estimation (RSE) of consumer electronic devices is always at risk of denial-of-service (DoS) attacks. Therefore, the adversarial strategy between consumer electronic devices and DoS attackers is critical. This paper focuses on the adversarial strategy between consumer electronic devices and DoS attackers in IoT-enabled RSE Systems. We first propose a remote joint estimation model for distributed measurements to effectively reduce consumer electronic device workload and minimize data leakage risks. The Kalman filter is deployed on the remote estimator, and the DoS attacks with open-loop as well as closed-loop are considered. We further introduce advanced reinforcement learning techniques, including centralized and distributed Minimax-DQN, to address high-dimensional decision-making challenges in both open-loop and closed-loop scenarios. Especially, the Q-network instead of the Q-table is used in the proposed approaches, which effectively solves the challenge of Q-learning. Moreover, the proposed distributed Minimax-DQN reduces the action space to expedite the search for Nash Equilibrium (NE). The experimental results validate that the proposed model can expeditiously restore the RSE error covariance to a stable state in the presence of DoS attacks, exhibiting notable attack robustness. The proposed centralized and distributed Minimax-DQN effectively resolves the NE in both open and closed-loop case, showcasing remarkable performance in terms of convergence. It reveals that substantial advantages in both efficiency and stability are achieved compared with the state-of-the-art methods.

Problem

• Balancing computational load and security in IoT-enabled RSE systems
• Mitigating DoS attack risks for consumer electronic edge devices
• Achieving Nash Equilibrium via reinforcement learning in adversarial scenarios

Innovation

• Remote joint estimation model reduces workload
• Minimax-DQN for high-dimensional decision-making
• Distributed Minimax-DQN expedites Nash Equilibrium search