在CPS环境中针对网络安全的集体自编码基于入侵检测的Sine-Cosine-Adopted African Vultures优化
Latifah Almuqren1, Fuad Al-Mutiri2, Mashael Maashi3
1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了一种新的网络安全技术,SCAVO-EAEID,用于网络物理系统 (CPS). 与集团自动编码器相结合的Sine-Cosine-Adopted African Vultures优化在检测网络入侵方面实现了99.20%的准确性.
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:
- 网络物理系统 (CPS) 越来越多地被使用,这带来了重大的安全挑战.
- 侵入检测系统 (IDS) 对于网络安全至关重要.
- 深度学习 (DL) 和人工智能 (AI) 提供先进的IDS功能,而元启发算法则有助于功能选择.
研究的目的:
- 提出一种新的技术,SCAVO-EAEID,用于加强CPS环境中的网络安全.
- 为了利用DL和AI进行强大的入侵检测.
- 为了有效的特征选择,使用元启发式算法.
主要方法:
- 这项研究介绍了以集体自编码器为基础的入侵检测 (SCAVO-EAEID) 技术优化侧鼻亲戚采用的非洲的优化技术.
- 它使用Z-score规范化进行预处理,并采用基于Sine-Cosine-Adopted African Vultures优化特征选择 (SCAVO-FS) 来进行最佳特征子集选择.
- 一组长短期内存自动编码器 (LSTM-AE) 模型用于入侵检测,并配有RMSProp优化器用于超参数调整.
主要成果:
- 拟议的SCAVO-EAEID技术在识别CPS.内的入侵方面表现出显著的表现.
- 对基准数据集的实验结果证实了该方法的有效性.
- 该技术达到99.20%的最大精度,超过了现有方法.
结论:
- SCAVO-EAEID技术为网络物理系统的入侵检测提供了一个高度准确和有效的解决方案.
- 集成先进的DL模型和元启发式优化显著提高了网络安全.
- 这些发现突出了拟议方法在现实世界CPS安全应用中的潜力.
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