IoTTPS:整合RKSVM基于模型的物联网威胁保护系统
Urooj Akram1, Wareesa Sharif1, Mobeen Shahroz1
1Department of Artificial Intelligence, The Islamia University of Bahawalpur, Bahawalpur 63100, Punjab, Pakistan.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种使用整体RKSVM模型的物联网威胁保护系统 (IoTTPS),以增强物联网 (IoT) 网络安全. 该IoTTPS有效地检测和缓解各种网络威胁的高精度.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 物联网网络由于轻量级传感器和缺乏强大的安全协议,因此很容易受到攻击.
- 威胁包括数据泄露,侵犯隐私和恶意行为者操纵数据.
- 现有的安全措施不足以应对物联网不断变化的威胁格局.
研究的目的:
- 为增强物联网网络安全提出物联网威胁保护系统 (IoTTPS).
- 开发一个整体机器学习模型 (RKSVM) 以有效检测威胁.
- 评估拟议系统对各种网络攻击的性能.
主要方法:
- 一个整体模型RKSVM被开发出来,结合了随机森林 (RF),K-最近邻居 (KNN) 和支持矢量机器 (SVM).
- 在基准物联网数据集上训练和测试机器学习模型:KDD Cup 99,NSL-KDD和CIC-IDS 2017.
- 用网格搜索超参数优化和K-Fold交叉验证来优化模型性能.
主要成果:
- 拟议的RKSVM模型实现了高分类准确度:DoS的99.7%,探测器的99.3%,U2R的99.7%,R2L攻击的97.8%.
- 在多个数据集中验证了性能,证明了一致的有效性.
- 整体RKSVM模型的性能优于其他测试过的机器学习算法.
结论:
- 开发的IoTTPS在检测和保护各种物联网网络威胁方面表现出卓越的性能.
- RKSVM 合奏模型为保护物联网环境提供了强大而准确的解决方案.
- 这些发现突出了整体机器学习在推进物联网安全方面的潜力.
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