Reinforce Security: A Model-Free Approach Towards Secure Wiretap Coding

TL;DR

This paper explores a model-free reinforcement learning approach, specifically policy gradient methods, to develop secure wiretap coding schemes that do not require channel knowledge, enhancing wireless communication security.

Contribution

It introduces a novel reinforcement learning-based method for secure encoding that combines co-set structure enforcement with recent RL techniques, evaluated through extensive simulations.

Findings

Eavesdropper decoding error is effectively capped.

Reinforcement learning improves secure encoding without channel knowledge.

The approach outperforms traditional methods in simulation scenarios.

Abstract

The use of deep learning-based techniques for approximating secure encoding functions has attracted considerable interest in wireless communications due to impressive results obtained for general coding and decoding tasks for wireless communication systems. Of particular importance is the development of model-free techniques that work without knowledge about the underlying channel. Such techniques utilize for example generative adversarial networks to estimate and model the conditional channel distribution, mutual information estimation as a reward function, or reinforcement learning. In this paper, the approach of reinforcement learning is studied and, in particular, the policy gradient method for a model-free approach of neural network-based secure encoding is investigated. Previously developed techniques for enforcing a certain co-set structure on the encoding process can be combined with recent reinforcement learning approaches. This new approach is evaluated by extensive simulations, and it is demonstrated that the resulting decoding performance of an eavesdropper is capped at a certain error level.