Wang Zhen, Zainal Anazida, Siraj Maheyzah Md, Ghaleb Fuad A, Hao Xue, Han Shaoyong
School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325035, Zhejiang, China.
Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Johor, 81310, Malaysia.
Sci Rep. 2025 Jan 14;15(1):1917. doi: 10.1038/s41598-024-85083-8.
The application of artificial neural networks (ANNs) can be found in numerous fields, including image and speech recognition, natural language processing, and autonomous vehicles. As well, intrusion detection, the subject of this paper, relies heavily on it. Different intrusion detection models have been constructed using ANNs. While ANNs are relatively mature to construct intrusion detection models, some challenges remain. Among the most notorious of these are the bloated models caused by the large number of parameters, and the non-interpretability of the models. Our paper presents Convolutional Kolmogorov-Arnold Networks (CKANs), which are designed to overcome these difficulties and provide an interpretable and accurate intrusion detection model. Kolmogorov-Arnold Networks (KANs) are developed from the Kolmogorov-Arnold representation theorem. Meanwhile, CKAN incorporates a convolutional computational mechanism based on KAN. The model proposed in this paper is constructed by incorporating attention mechanisms into CKAN's computational logic. The datasets CICIoT2023 and CICIoMT2024 were used for model training and validation. From the results of evaluating the performance indicators of the experiments, the intrusion detection model constructed based on CKANs has an attractive application prospect. As compared with other methods, the model can predict a much higher level of accuracy with significantly fewer parameters. However, it is not superior in terms of memory usage, execution speed and energy consumption.
人工神经网络（ANNs）的应用见于众多领域，包括图像与语音识别、自然语言处理以及自动驾驶车辆。同样，本文的主题——入侵检测，也严重依赖于它。人们已使用人工神经网络构建了不同的入侵检测模型。虽然人工神经网络在构建入侵检测模型方面相对成熟，但仍存在一些挑战。其中最突出的是大量参数导致的模型臃肿，以及模型的不可解释性。我们的论文提出了卷积柯尔莫哥洛夫 - 阿诺德网络（CKANs），旨在克服这些困难，并提供一个可解释且准确的入侵检测模型。柯尔莫哥洛夫 - 阿诺德网络（KANs）是基于柯尔莫哥洛夫 - 阿诺德表示定理开发的。同时，CKAN在KAN的基础上融入了卷积计算机制。本文提出的模型是通过将注意力机制纳入CKAN的计算逻辑构建而成的。数据集CICIoT2023和CICIoMT2024用于模型训练和验证。从实验性能指标评估结果来看，基于CKANs构建的入侵检测模型具有诱人的应用前景。与其他方法相比，该模型能用显著更少的参数预测出更高的准确率。然而，它在内存使用、执行速度和能耗方面并不占优。
