When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions

TL;DR

This paper explores how machine learning can enhance wireless security by enabling adaptive attack and defense strategies, addressing challenges and outlining future research directions in the field.

Contribution

It provides a comprehensive overview of ML applications in wireless security, highlighting current solutions, challenges, and a roadmap for future research.

Findings

ML enables automated attack detection and defense in wireless systems.

Emerging adversarial ML techniques pose new security challenges.

A roadmap guides future research in ML-driven wireless security.

Abstract

Wireless systems are vulnerable to various attacks such as jamming and eavesdropping due to the shared and broadcast nature of wireless medium. To support both attack and defense strategies, machine learning (ML) provides automated means to learn from and adapt to wireless communication characteristics that are hard to capture by hand-crafted features and models. This article discusses motivation, background, and scope of research efforts that bridge ML and wireless security. Motivated by research directions surveyed in the context of ML for wireless security, ML-based attack and defense solutions and emerging adversarial ML techniques in the wireless domain are identified along with a roadmap to foster research efforts in bridging ML and wireless security.