A Deep Ensemble-Based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification

Rajeev Sahay, Christopher G. Brinton, David J. Love

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Deep learning-based automatic modulation classification (AMC) models are susceptible to adversarial attacks. Such attacks inject specifically crafted wireless interference into transmitted signals to induce erroneous classification predictions. Furthermore, adversarial interference is transferable in black box environments, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification model. In this work, we propose a novel wireless receiver architecture to mitigate the effects of adversarial interference in various black box attack environments. We begin by evaluating the architecture uncertainty environment, where we show that adversarial attacks crafted to fool specific AMC DL architectures are not directly transferable to different DL architectures. Next, we consider the domain uncertainty environment, where we show that adversarial attacks crafted on time domain and frequency domain features to not directly transfer to the altering domain. Using these insights, we develop our Assorted Deep Ensemble (ADE) defense, which is an ensemble of deep learning architectures trained on time and frequency domain representations of received signals. Through evaluation on two wireless signal datasets under different sources of uncertainty, we demonstrate that our ADE obtains substantial improvements in AMC classification performance compared with baseline defenses across different adversarial attacks and potencies.

Original languageEnglish (US)
Pages (from-to)71-85
Number of pages15
JournalIEEE Transactions on Cognitive Communications and Networking
Volume8
Issue number1
DOIs
StatePublished - Mar 1 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

Keywords

  • Adversarial attacks
  • automatic modulation classification
  • machine learning in communications
  • wireless security

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