注意力变压器深度学习算法用于对物联网系统的入侵检测,使用自动Xplainable功能选择.
Demóstenes Zegarra Rodríguez1, Ogobuchi Daniel Okey2, Siti Sarah Maidin3
1Department of Computer Science, Federal University of Lavras, Minas Gerais, Brazil.
PloS one
|October 16, 2023
概括
本研究介绍了TabNet-IDS,这是一款用于物联网 (IoT) 的入侵检测系统,它使用专注的功能选择机制. 它在检测常见表格数据集上的网络威胁方面实现了高准确性.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 物联网 (IoT) 安全 安全 物联网
背景情况:
- 物联网 (IoT) 和工业物联网 (IIoT) 系统的普及增加了安全风险,特别是来自拒绝服务 (DoS) 和分布式拒绝服务 (DDoS) 攻击.
- 现有的入侵检测系统 (IDS) 经常在机器学习任务中常见的表格数据格式中扎,在模型可解释性和特征选择方面面临挑战.
研究的目的:
- 为物联网安全提出智能入侵检测系统 (IDS),以解决表格数据上的传统深度学习模型的局限性.
- 开发一种模型,利用注意力机制自动选择突出特征,并提供可解释的结果.
主要方法:
- 在PyTorch深度学习框架内使用TabNet算法实现TabNet-IDS模型.
- 利用TabNet中的注意力机制进行自动特征选择和增强模型可解释性.
- 评估模型在基准数据集上的表现:CIC-IDS2017,CSE-CICIDS2018,以及CIC-DDoS2019.这些数据集中的表现.
主要成果:
- 在测试的数据集上,TabNet-IDS模型实现了高准确率:在CIC-IDS2017上达到了97%;在CSE-CICIDS2018上达到了95%;在CIC-DDoS2019上达到了98%
- 使用表格数据展示了TabNet架构在物联网环境中用于入侵检测的有效性.
- 该模型的注意力机制提供了可解释的特征选择,有助于更好地理解模型.
结论:
- TabNet是一个可行的和有效的深度学习架构,用于对物联网安全中的表格数据集进行入侵检测.
- 拟议的TabNet-IDS提供了一个强大的解决方案,通过精确检测网络威胁,提供可解释的见解来提高物联网安全性.
- 未来的工作可以探索基于注意力的模型的进一步优化和应用,以应对先进的网络安全挑战.
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