SCSLnO-SqueezeNet:Sine Cosine-Sea Lion优化启用了SqueezeNet用于物联网中的入侵检测
M Masthan1, K Pazhanikumar2, Meena Chavan3
1Technical Project Manager, Arffy Technologies, Karnataka, India.
Network (Bristol, England)
|October 9, 2023
概括
这项研究介绍了一种新的物联网 (IoT) 威胁检测方法,使用SqueezeNet模型与Sine Cosine Sea Lion Optimization (SCSLnO) 进行了优化,在识别网络入侵方面实现了卓越的准确性.
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:
- 智能生态系统和物联网 (IoT) 面临着重大的安全和隐私挑战.
- 有效的入侵检测系统对于保护物联网环境至关重要.
研究的目的:
- 开发一个先进的物联网威胁检测模型.
- 使用混合优化算法来提高入侵检测的准确性.
主要方法:
- 一个SqueezeNet模型被用于物联网威胁检测.
- 在网络分类器培训中使用了Sine Cosine海狮优化 (SCSLnO),结合了Sine Cosine算法 (SCA) 和海狮优化 (SLnO).
- 豪斯多夫距离被用于特征选择.
主要成果:
- 与现有方法相比,SCSLnO优化的SqueezeNet模型在BoT-IoT数据集上表现出更好的准确性.
- 在90%的培训率下,设计的方法比PSO-KNN/SVM提高了10.75%,比Voting Ensemble Classifier提高了8.45%,比Deep NN提高了6.36%,比Deep Learning提高了3.51%.
结论:
- 拟议的基于SCSLnO的SqueezeNet模型为物联网威胁检测提供了一个高度有效的解决方案.
- 这种方法显著提高了物联网生态系统中的网络安全和隐私.
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