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(物联网) 网络入侵检测系统使用优化算法

¹Department of Physics and Electronic Information Engineering, LYU Liang University, LV Liang, 033001, Shanxi, China. 15364586856@163.com.

Scientific reports

|July 2, 2025

概括

本研究介绍了一种混合鱼优化算法 (WOA) 和灰狼优化 (GWO) 框架,用于高效的物联网网络入侵检测. WOA-GWO模型在资源有限的环境中提高了威胁识别准确性和实时响应能力.

科学领域:

网络安全网络安全
人工智能的人工智能
群集情报群集情报群集情报

背景情况:

物联网 (IoT) 环境面临复杂的网络入侵检测挑战.
现有的方法往往在很大的特征空间和缓慢的融合方面扎.
单一优化算法容易受到局部优化的影响.

研究的目的:

为增强物联网网络入侵检测提出混合智能框架 (WOA-GWO).
为了更好的优化,平衡全球勘探和当地开发.
克服单个算法方法在检测各种网络威胁方面的局限性.

主要方法:

将鱼优化算法 (WOA) 和灰狼优化 (GWO) 集成到混合WOA-GWO框架中.
利用WOA的螺旋泡网战略进行全球优化,并利用GWO的社会层次来实现本地利用.
实现动态特征选择和参数优化,以提高检测灵敏度和概括性.

主要成果:

与LSTM-RNN和SVM相比,WOA-GWO框架显示出更高的灵敏度和通用性.
由于轻量化设计,在二进制分类任务中实现了卓越的实时响应.
通过自适应性特征权重分配,有效地缓解了多类攻击识别中的特征混.

结论:

混合WOA-GWO算法为物联网安全中的有效威胁检测提供了一个新的方法基础.
群体智能算法显示了保护资源有限的物联网环境的巨大潜力.
拟议的框架为入侵检测中的动态特征选择和参数优化提供了一个强大的解决方案.

关键词:

网络安全网络安全灰狼优化优化灰狼优化物联网的物联网,就是物联网.侵入检测系统的入侵检测系统鱼优化算法鱼优化算法

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