Multi-agent Deep Reinforcement Learning-based Key Generation for Graph Layer Security

doi:10.1145/3711900

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Multi-agent Deep Reinforcement Learning-based Key Generation for Graph Layer Security

2025-02-22

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DOI：10.1145/3711900

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Abstract

En 中文

Recently, the emergence of Internet of Things (IoT) devices has posed a challenge for securing information and avoiding attacks. Most of the cryptography solutions are based on physical layer security (PLS), whose idea is to fully exploit the properties of wireless channel state information (CSI) for generating symmetric keys between two communication nodes. However, accurate channel estimation is vulnerable for attackers and relies on powerful signal processing capability, which is not suitable for low-power IoT devices. In this article, we expect to apply graph layer security (GLS) to exploit the common features of physical dynamics detected by IoT sensors placed in networked systems to generate keys for data encryption and decryption, which we believe is a new frontier to security for both industry and academic research. We propose a distributed key generation algorithm based on multi-agent deep reinforcement learning (MADRL) approach, which enables communication nodes to cooperatively generate symmetric keys based on their locally detected physical dynamics (e.g., water/gas/oil/electrical pressure/flow/voltage) with low computational complexity and without information exchange. In order to demonstrate the feasibility, we conduct and evaluate our key generation algorithm in both a simulated and real water distribution network. The experimental results show that the proposed algorithm has considerable performance in terms of randomness, bit agreement rate (BAR), and so on.

Keywords:

Multi-agent deep reinforcement learning

Physical layer security

graph layer security

IoT devices

and physical dynamics

Journal

ACM Transactions on Privacy and Security

IF:

2.8

Papers:

293

Citations:

770

Organization

No organization information available

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