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Position Papers of the 19th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 40

Resource efficient Internet-of-Things intrusion detection with spiking neural networks

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DOI: http://dx.doi.org/10.15439/2024F8800

Citation: Position Papers of the 19th Conference on Computer Science and Intelligence Systems, M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 40, pages 73–78 ()

Full text

Abstract. Spiking neural networks are a novel implementation of artificial neural networks that is closely based on neurobiology. We created a spiking neural network classifier in PyTorch and snn-torch based on Leaky Integrate-and-Fire neurons that managed to get F1 score of 0.957 on 10 000 samples of BoT-IoT dataset and 240 hidden spiking neurons. Training was performed on CPU for 300 epochs and 10 simulation steps per epoch, utilizing Adam optimizer, cross-entropy loss and back-propagation as a learning algorithm. Lowering hidden spiking neuron count from 240 to 72 and sample size from 10 000 to 1 000 we were able to optimize training time by 84\% and testing time by 57\% while having F1 score of 0.944. Loss, Receiver Operating Characteristic and Precision Recall curves are shown for the two experiments, as well as summarized data for additional performed experiments with different sample sizes and neuron counts. We conclude that spiking neural networks for intrusion detection represent a viable solution for training and classification on resource constrained devices with limited samples. Further research steps are presented with the goal of improving performance.

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