一个基于Convolutional Kolmogorov-Arnold网络的入侵检测模型
Zhen Wang1,2, Anazida Zainal2, Maheyzah Md Siraj2
1School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325035, Zhejiang, China.
Scientific reports
|January 14, 2025
概括
卷积科尔摩戈罗夫-阿诺德网络 (CKAN) 为网络入侵检测提供了一个可解释和准确的解决方案. 这种新的方法显著减少模型参数,同时保持高预测准确度,解决传统人工神经网络的关键局限性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 人工神经网络 (ANN) 广泛用于入侵检测,但由于模型大小和缺乏可解释性而受到影响.
- 现有的ANN存在诸如参数膨胀和网络安全应用中的不透明决策过程等挑战.
研究的目的:
- 引入卷积科尔摩戈罗夫-阿诺德网络 (CKANs) 作为一种新,可解释和准确的入侵检测模型.
- 解决传统ANN在模型大小和入侵检测的可解释性方面的局限性.
主要方法:
- 根据科尔莫戈罗夫-阿诺德表示定理开发了卷积科尔莫戈罗夫-阿诺德网络 (CKAN).
- 将注意力机制集成到 CKAN 架构中,以增强计算逻辑.
- 使用CICIoT2023和CICIoMT2024数据集进行模型培训和验证.
主要成果:
- 与其他方法相比,基于CKAN的入侵检测模型表现出高准确度,参数显著减少.
- 由于提高了准确性和降低了模型复杂性,在入侵检测中实现了有吸引力的应用前景.
- 虽然参数效率很高,但该模型在内存使用,执行速度或能源消耗方面没有超过现有方法.
结论:
- 在开发可解释和准确的入侵检测系统方面,CKAN是一个有希望的进步.
- 拟议的模型有效地解决了传统ANN固有的参数膨胀和解释性问题.
- 可能需要进一步的研究来优化CKAN,以提高其在内存,速度和能耗方面的效率.
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