ROAST-IoT:在物联网网络中进行入侵检测的新型范围优化的注意力卷积分散技术
Anandaraj Mahalingam1, Ganeshkumar Perumal2, Gopalakrishnan Subburayalu3
1Department of Information Technology, PSNA College of Engineering and Technology, Dindigul 624622, Tamil Nadu, India.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究介绍了ROAST-IoT,这是一个用于物联网 (IoT) 的新型入侵检测系统. 它通过使用机器学习来提高安全性,以便在复杂的物联网网络中更快,更准确地检测威胁.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 物联网 (IoT) 由于系统复杂性和数据量而带来了重大安全挑战.
- 现有的物联网入侵检测系统 (IDS) 往往存在检测不足,高延迟和延长处理时间的问题.
- 机器学习 (ML) 在物联网IDS中被广泛采用,但性能限制仍然存在.
研究的目的:
- 为物联网网络提出一种新且高效的入侵检测系统.
- 解决现有的物联网安全框架的局限性,特别是延迟和处理时间.
- 提高物联网环境中入侵检测的准确性和可靠性.
主要方法:
- 开发了范围优化的注意力卷积分散技术 (ROAST-IoT).
- 利用散射范围特征选择 (SRFS) 进行关键特征提取.
- 采用基于注意力的卷积前网络 (ACFN) 来进行入侵分类.
- 使用修改后的Dingo优化 (MDO) 算法优化了分类器的损失函数.
主要成果:
- 在多个基准数据集 (ToN-IoT,IoT-23,UNSW-NB 15,Edge-IIoT) 中,ROAST-IoT展示了卓越的性能.
- 实现了高准确率,包括在IoT-23上达到99.15%,ToN-IoT上达到99.78%,UNSW-NB 15上达到99.88%,Edge-IIoT上达到99.45%.
- 实现了0.998的平均AUC-ROC,这表明正常和攻击流量之间有很好的区分.
结论:
- ROAST-IoT有效且可靠地检测对物联网系统的入侵攻击.
- 拟议的技术显著优于现有的最先进的入侵检测系统.
- ROAST-IoT为提高复杂物联网网络的安全态度提供了一个有前途的解决方案.
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