论文标题
带有单尖峰颞编码神经元的尖峰神经网络用于网络入侵检测
Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection
论文作者
论文摘要
尖峰神经网络（SNN）由于其强大的生物质量和高能量效率而引人入胜。但是，它的性能远远落后于常规深度神经网络（DNNS）。在本文中，考虑了一类单尖峰颞编码的集成和开火神经元，我们分析了漏水和非裂解神经元的输入输出表达式。我们表明，由泄漏的神经元建造的SNN遭受了过度非线性和过度复杂的输入输出响应，这是他们训练艰难和性能低下的主要原因。这个原因比普遍认为的无与伦比的尖峰问题更为基本。为了支持这一说法，我们表明使用非裂口神经元构建的SNN可以具有较小的复杂和非固定性输入输出响应。它们可以容易训练并具有出色的性能，这可以通过在两个流行的网络入侵检测数据集（即NSL-KDD和AWID数据集）上实验SNN来证明。我们的实验结果表明，拟议的SNN的表现优于DNN模型和经典机器学习模型的全面列表。本文表明，与共同信念相比，SNN可以具有希望和竞争性。
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.
