滑动主组件和动态奖励增强学习基于IIoT攻击检测
Vijayan Ellappan1, Anand Mahendran2, Murali Subramanian2
1School of Information Technology and Engineering, VIT, Vellore, 632014, India.
Scientific reports
|November 27, 2023
概括
一种新的强化学习方法,SPC-DRRL,通过减少攻击检测错误来提高工业物联网 (IIoT) 的安全性. 这种先进的系统提高了识别IIoT网络威胁的准确性和效率.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 事物的工业互联网 (IIoT)
背景情况:
- 物联网 (IoT) 和工业物联网 (IIoU) 越来越容易受到网络威胁,需要强大的攻击检测系统.
- 现有的方法在较高的错误率和在大型IIoT数据集中检测多种攻击方面存在困难.
研究的目的:
- 引入一种基于强化学习的新型攻击检测方法,滑动主组件和动态奖励强化学习 (SPC-DRRL),以提高IIoT网络安全.
- 为了提高检测各种IIoT网络攻击的准确性和降低错误率.
主要方法:
- 在TON_IoT数据集上使用min-max规范化进行数据预处理.
- 通过具有滑动窗口的滑动主要组件算法进行特征提取.
- 开发一个动态的奖励强化学习模型,并建立一个适应性训练的事件存储库.
主要成果:
- 该SPC-DRRL系统显著减少了IIoT攻击检测时间,开销和错误率.
- 与传统的强化学习方法相比,在攻击检测方面取得了更高的准确性.
- 在ToN_IoT数据集上验证的有效性.
结论:
- 拟议的SPC-DRRL方法为保护IIoT网络免受网络威胁提供了更有效和高效的解决方案.
- 这种方法通过将检测错误最小化和提高整体系统性能来显著改进现有方法.
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