Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection
This work addresses the low performance of SNNs for network intrusion detection, offering a potential solution to make them competitive with conventional methods, though it is incremental in improving a specific neuron type.
The paper tackled the performance gap between spiking neural networks (SNNs) and deep neural networks (DNNs) by analyzing single-spike temporal-coded neurons, showing that leaky neurons cause overly complex input-output responses that hinder training. It demonstrated that nonleaky neurons reduce this complexity, enabling SNNs to outperform DNNs and classic models on network intrusion detection datasets like NSL-KDD and AWID.
Spiking neural network (SNN) is interesting due to its strong bio-plausibility and high energy efficiency. However, its performance is falling far behind conventional deep neural networks (DNNs). In this paper, considering a general class of single-spike temporal-coded integrate-and-fire neurons, we analyze the input-output expressions of both leaky and nonleaky neurons. We show that SNNs built with leaky neurons suffer from the overly-nonlinear and overly-complex input-output response, which is the major reason for their difficult training and low performance. This reason is more fundamental than the commonly believed problem of nondifferentiable spikes. To support this claim, we show that SNNs built with nonleaky neurons can have a less-complex and less-nonlinear input-output response. They can be easily trained and can have superior performance, which is demonstrated by experimenting with the SNNs over two popular network intrusion detection datasets, i.e., the NSL-KDD and the AWID datasets. Our experiment results show that the proposed SNNs outperform a comprehensive list of DNN models and classic machine learning models. This paper demonstrates that SNNs can be promising and competitive in contrast to common beliefs.