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在物联网环境中使用基于机器学习的网络安全解决方案进行二进制猎人-猎物优化

Adil O Khadidos¹, Zenah Mahmoud AlKubaisy^2,3, Alaa O Khadidos^4,5

¹Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)

|August 26, 2023

概括

本研究介绍了一种新的机器学习方法,用于检测物联网 (IoT) 中的网络鱼攻击. 基于机器学习的网络鱼攻击检测 (BHPO-MLPAD) 的二进制猎人猎人优化有效地识别和减轻这些安全威胁.

科学领域:

计算机科学计算机科学
网络安全网络安全
人工智能的人工智能

背景情况:

物联网 (IoT) 促进了日常物体的连接,导致了广泛的技术集成.
然而,物联网设备的快速扩张引入了重要的安全漏洞,使他们容易受到网络威胁.
针对欺诈性数据提取的网络鱼攻击越来越多地针对物联网设备,构成越来越大的风险.

研究的目的:

开发一种有效的方法来检测特定于物联网 (IoT) 环境中的网络鱼攻击.
增强物联网网络和设备的安全性,防止复杂的网络鱼诈骗.
使用机器学习和优化算法来提高网络鱼攻击检测的准确性和效率.

主要方法:

建议使用基于机器学习的网络鱼攻击检测 (BHPO-MLPAD) 方法进行二进制猎人-猎物优化.
使用BHPO算法进行最佳特征选择,识别攻击检测的关键数据点.
一个级联前进神经网络 (CFNN) 模型,通过可变步骤果优化 (VFFO) 算法进行优化,执行网络鱼攻击分类.

主要成果:

与现有的方法相比,BHPO-MLPAD技术在识别网络鱼攻击方面表现出更高的性能.
该方法在物联网环境中的特征选择和分类任务中实现了高精度.
在基准数据集上的评估证实了在各种绩效指标上提出的方法的有效性.

结论:

在物联网环境中,BHPO-MLPAD方法为检测网络鱼攻击提供了强大的解决方案.
集成先进的优化和机器学习技术显著改善了连接设备的网络安全.
这项研究有助于保护物联网不断扩大的景观免受不断变化的网络威胁.

关键词:

物联网的物联网,就是物联网.功能选择功能选择猎人猎物的优化猎人猎物的优化机器学习是机器学习.网络鱼攻击的攻击.

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