A revolutionary approach to use convolutional spiking neural networks for robust intrusion detection

Abstract

In an era dominated by network connectivity, the reliance on robust and secure networks has become paramount. With the advent of 5G and the Internet of Things, networks are expanding in both scale and complexity, rendering them susceptible to a myriad of cyber threats. This escalating risk encompasses potential breaches of user privacy, unauthorized access to transmitted data, and targeted attacks on the underlying network infrastructure. To safeguard the integrity and security of modern networked societies, the deployment of Network Intrusion Detection Systems is imperative. This paper presents a novel lightweight detection model that seamlessly integrates Spiking Neural Networks and Convolutional Neural Networks with advanced algorithmic frameworks. Leveraging this hybrid approach, the proposed model achieves superior detection accuracy while maintaining efficiency in terms of power consumption and computational resources. This paper presents a new style recognition model that seamlessly integrates spiking neural networks and convolutional neural networks with advanced algorithmic frameworks. We call this combined method Spiking-HCCN. Using this hybrid approach, Spiking-HCCN achieves superior detection accuracy while maintaining efficiency in terms of power consumption and computational resources. Comparative evaluations against state-of-the-art models, including Spiking GCN and Spike-DHS, demonstrate significant performance advantages. Spiking-HCCN outperforms these benchmarks by 24% in detection accuracy, 21% in delay, and 29% in energy efficiency, underscoring its efficacy in fortifying network security in the face of evolving cyber threats.