mlsanityData to 14 Oct 2026

Spiking Neural Network: a low power solution for physical layer authentication

arXiv 2505.12647 ↗First posted 19 May 2025cs.LGcited by 0

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Abstract (arXiv)

Deep learning (DL) is a powerful tool that can solve complex problems, and thus, it seems natural to assume that DL can be used to enhance the security of wireless communication. However, deploying DL models to edge devices in wireless networks is challenging, as they require significant amounts of computing and power resources. Notably, Spiking Neural Networks (SNNs) are known to be efficient in terms of power consumption, meaning they can be an alternative platform for DL models for edge devices. In this study, we ask if SNNs can be used in physical layer authentication. Our evaluation suggests that SNNs can learn unique physical properties (i.e., `fingerprints') of RF transmitters and use them to identify individual devices. Furthermore, we find that SNNs are also vulnerable to adversarial attacks and that an autoencoder can be used clean out adversarial perturbations to harden SNNs against them.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 19 May 2025.

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References · 17

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  3. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 626
  4. 2017Adversarial Examples: Attacks and Defenses for Deep Learningcited by 351
  5. 2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 279
  6. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 266
  7. 2018Adversarial Attacks and Defences: A Surveycited by 259
  8. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 245
  9. 2021Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networkscited by 139
  10. 2023Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networkscited by 122
  11. 2018Towards the first adversarially robust neural network model on MNISTcited by 104
  12. 2019advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorchcited by 69
5 more
  1. 2023Robust Evaluation of Diffusion-Based Adversarial Purificationcited by 65
  2. 2021Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Themcited by 48
  3. 2016Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networkscited by 31
  4. 2024Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methodscited by 25
  5. 2020Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerationscited by 16

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