Frequency Selective Adversarial Attack Against Deep Learning Based Wireless Signal Classifiers

Wireless communication is the foundation of modern systems and enables critical applications in the military, commercial and civil spheres. Its increasing prevalence has changed daily life and operations around the world, while introducing serious security threats. Attackers exploit these vulnerabilities to intercept sensitive data, disrupt communications, or conduct targeted attacks, compromising confidentiality and functionality.

While encryption is a critical component of secure communication, it is often insufficient in situations involving resource-constrained devices, such as IoT systems, or in the face of advanced hostile techniques. New solutions, including signal jamming optimization, autoencoders for preprocessing, and narrowband adversarial designs, aim to fool attackers without significantly affecting the bit error rate. Despite progress, challenges remain in ensuring robustness in real-world scenarios and for resource-constrained devices.

To address those challenges, a recently published paper presents an innovative strategy to attack wireless signal classifiers by exploiting frequency-based adversarial attacks. The authors highlight the vulnerability of communication systems to carefully designed disturbances capable of masking modulation signals while allowing the legitimate receiver to decode the message. The main novelty of the article is the imposition of limitations on the frequency content of the disturbances. The authors recognize that traditional adversarial attacks often produce high-frequency noise that communication systems can easily filter out. As a result, they optimize adverse disturbances so that they are focused on a limited frequency band that the intruder's filters cannot detect or suppress.

Specifically, the adversarial attack is framed as an optimization problem that aims to maximize the misclassification rate of the intruder classifier while keeping the perturbation power below a certain threshold. The authors propose to use adversarial training techniques and gradient-based methods to calculate perturbations. In particular, they derive a closed-form solution to the perturbation that respects the constraints imposed by the filtering process. Furthermore, the method uses the Discrete Fourier Transform (DFT) to decompose the signal into the frequency domain. This allows for a filter that only lets through the relevant frequency components, thus creating specific disturbances that communication systems will not filter out.

Two specific attack algorithms are presented in the paper: Frequency Selective PGD (FS-PGD) and Frequency Selective C&W (FS-C&W), which are adaptations of existing gradient-based attack methods tailored to the challenges they pose. wireless communications.

The research team proposed to evaluate the effectiveness of FS-PGD and FS-C&W against deep learning-based modulation classifiers. The experiments used ten modulation schemes and 2720 data blocks per type. A ResNet18 classifier was used and FS-PGD and FS-C&W were compared with traditional adversarial methods such as FGSM and PGD. The results showed that FS-PGD and FS-C&W achieved high deception rates (99.98% and 99.96%, respectively) and maintained robust performance after filtering, with minimal disturbance detectable by the filters. These methods were also robust to adversarial training and filtered out bandwidth discrepancies. The findings confirm that FS-PGD and FS-C&W effectively fool classifiers while preserving signal integrity, making them viable for real-world wireless communication applications.

In conclusion, the study demonstrates that the proposed frequency-selective adversary attack methods, FS-PGD and FS-C&W, offer a robust solution to fool deep learning-based modulation classifiers without significantly affecting the communication signal. By focusing disturbances within a restricted frequency band, these methods overcome the traditional limitations of adversarial attacks, which often involve high-frequency noise that can be easily filtered out. Experimental results confirm the effectiveness of FS-PGD and FS-C&W in achieving high deception rates and resilience to various filtering techniques and adversarial training scenarios. This highlights its potential for real-world applications, where secure communication is essential, and offers valuable insights to develop more secure wireless communication systems in the face of evolving threats.

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