# Securing IoT Communication using Physical Sensor Data -- Graph Layer Security with Federated Multi-Agent Deep Reinforcement Learning

## TL;DR

This paper introduces a novel Graph Layer Security approach for IoT communication that leverages federated multi-agent deep reinforcement learning to generate secure keys from correlated physical sensor data, enhancing security without relying on traditional channel estimation.

## Contribution

It proposes a federated multi-agent deep reinforcement learning scheme for distributed key generation based on physical sensor data correlations, a first in the context of Graph Layer Security.

## Key findings

- Achieved high key agreement rate (KAR) in experiments.
- Demonstrated improved key randomness and security performance.
- Validated the approach with initial experimental results.

## Abstract

Internet-of-Things (IoT) devices are often used to transmit physical sensor data over digital wireless channels. Traditional Physical Layer Security (PLS)-based cryptography approaches rely on accurate channel estimation and information exchange for key generation, which irrevocably ties key quality with digital channel estimation quality. Recently, we proposed a new concept called Graph Layer Security (GLS), where digital keys are derived from physical sensor readings. The sensor readings between legitimate users are correlated through a common background infrastructure environment (e.g., a common water distribution network or electric grid). The challenge for GLS has been how to achieve distributed key generation. This paper presents a Federated multi-agent Deep reinforcement learning-assisted Distributed Key generation scheme (FD2K), which fully exploits the common features of physical dynamics to establish secret key between legitimate users. We present for the first time initial experimental results of GLS with federated learning, achieving considerable security performance in terms of key agreement rate (KAR), and key randomness.