Wang Zhen, Ghaleb Fuad A, Zainal Anazida, Siraj Maheyzah Md, Lu Xing
Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, 81310, Johor, Malaysia.
School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325035, Zhejiang, China.
Sci Rep. 2024 Mar 25;14(1):7054. doi: 10.1038/s41598-024-57691-x.
Many intrusion detection techniques have been developed to ensure that the target system can function properly under the established rules. With the booming Internet of Things (IoT) applications, the resource-constrained nature of its devices makes it urgent to explore lightweight and high-performance intrusion detection models. Recent years have seen a particularly active application of deep learning (DL) techniques. The spiking neural network (SNN), a type of artificial intelligence that is associated with sparse computations and inherent temporal dynamics, has been viewed as a potential candidate for the next generation of DL. It should be noted, however, that current research into SNNs has largely focused on scenarios where limited computational resources and insufficient power sources are not considered. Consequently, even state-of-the-art SNN solutions tend to be inefficient. In this paper, a lightweight and effective detection model is proposed. With the help of rational algorithm design, the model integrates the advantages of SNNs as well as convolutional neural networks (CNNs). In addition to reducing resource usage, it maintains a high level of classification accuracy. The proposed model was evaluated against some current state-of-the-art models using a comprehensive set of metrics. Based on the experimental results, the model demonstrated improved adaptability to environments with limited computational resources and energy sources.
已经开发了许多入侵检测技术，以确保目标系统能够在既定规则下正常运行。随着物联网（IoT）应用的蓬勃发展，其设备资源受限的特性使得探索轻量级且高性能的入侵检测模型变得迫在眉睫。近年来，深度学习（DL）技术得到了特别广泛的应用。脉冲神经网络（SNN）作为一种与稀疏计算和固有时间动态相关的人工智能类型，被视为下一代深度学习的潜在候选者。然而，应当指出的是，目前对SNN的研究主要集中在不考虑计算资源有限和电源不足的场景。因此，即使是最先进的SNN解决方案往往也效率低下。本文提出了一种轻量级且有效的检测模型。借助合理的算法设计，该模型整合了SNN以及卷积神经网络（CNN）的优势。除了减少资源使用外，它还保持了较高的分类准确率。使用一套全面的指标对所提出的模型与一些当前最先进的模型进行了评估。基于实验结果，该模型展示了对计算资源和能源有限的环境的更好适应性。
