Adversarial Threats to Automatic Modulation Open Set Recognition in Wireless Networks

TL;DR

This paper investigates the vulnerabilities of Automatic Modulation Open Set Recognition (AMOSR) in wireless networks to adversarial attacks, revealing that even robust models are susceptible to carefully crafted adversarial examples.

Contribution

It introduces the first adversarial attack framework for AMOSR, including threat models and attack methods, highlighting security vulnerabilities in existing AMOSR systems.

Findings

AMOSR models are vulnerable to adversarial examples

Proposed OSFGSM and OSPGD effectively reduce AMOSR performance

AMOR models show increased resistance to conventional interference but remain vulnerable to attacks

Abstract

Automatic Modulation Open Set Recognition (AMOSR) is a crucial technological approach for cognitive radio communications, wireless spectrum management, and interference monitoring within wireless networks. Numerous studies have shown that AMR is highly susceptible to minimal perturbations carefully designed by malicious attackers, leading to misclassification of signals. However, the adversarial security issue of AMOSR has not yet been explored. This paper adopts the perspective of attackers and proposes an Open Set Adversarial Attack (OSAttack), aiming at investigating the adversarial vulnerabilities of various AMOSR methods. Initially, an adversarial threat model for AMOSR scenarios is established. Subsequently, by analyzing the decision criteria of both discriminative and generative open set recognition, OSFGSM and OSPGD are proposed to reduce the performance of AMOSR. Finally, the influence of OSAttack on AMOSR is evaluated utilizing a range of qualitative and quantitative indicators. The results indicate that despite the increased resistance of AMOSR models to conventional interference signals, they remain vulnerable to attacks by adversarial examples.