Black-Box Adversarial Attacks on Spiking Neural Network for Time Series Data

This paper examines the vulnerability of spiking neural networks (SNNs) trained on time series data to adversarial attacks by employing artificial neural networks as surrogate models. We specifically explore the use of a 1D Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) netwo...

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Bibliographic Details

Published in:	2024 International Conference on Neuromorphic Systems (ICONS) pp. 229 - 233
Main Authors:	Hutchins, Jack, Ferrer, Diego, Fillers, James, Schuman, Catherine
Format:	Conference Proceeding
Language:	English
Published:	IEEE 30.07.2024
Subjects:	Accuracy Adversarial Attacks Black-Box Attack Closed box Convolutional neural networks Data models Data processing Long short term memory Neuromorphics Security Spiking neural networks Time series analysis
Online Access:	Get full text
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