相关实验视频
增强网络威胁防御机制使用组合的表征学习与二进制埃博拉优化搜索在物联网环境环境中的搜索
Meshari H Alanazi1, Jawad Hasan Alkhateeb2, Hayam Alamro3
1Department of Computer Science, College of Sciences, Northern Border University, Arar, Saudi Arabia.
Scientific reports
|September 26, 2025
概括
这项研究介绍了物联网 (IoT) 的新型网络威胁防御机制,在检测网络攻击方面达到99.29%的准确性. 二元埃博拉优化搜索算法和组合模型 (CDM-BEOSAEM) 方法增强了对不断变化的数字威胁的安全性.
科学领域:
- 网络安全和网络工程 网络安全和网络工程
- 人工智能和机器学习
- 物联网 (IoT) 安全 安全 物联网
背景情况:
- 不断变化的数字环境呈现出越来越复杂的恶意软件防御和攻击,往往超过传统的安全系统.
- 物联网 (IoT) 提供了显著的优势,但引入了漏洞,包括拒绝服务 (DoS) 的风险和对隐私,机密性和可用性的威胁.
- 现有的安全措施在相互连接的环境中难以跟上网络威胁的复杂性.
研究的目的:
- 提出一种先进的网络威胁防御机制,即使用二进制埃博拉优化搜索算法和组合模型 (CDM-BEOSAEM) 的网络威胁防御机制,专门设计用于改善物联网环境中的网络攻击检测.
- 为了提高识别和减轻复杂和快速扩展的物联网生态系统中的网络威胁的准确性和效率.
主要方法:
- 使用min-max规范化进行数据预处理,以实现最佳的输入格式.
- 使用二进制埃博拉优化搜索算法 (BEOSA) 识别最相关的特征.
- 网络威胁分类使用双向门式反复单元 (BiGRU),自动编码器 (AE) 和图形卷积网络 (GCN) 模型.
- 通过逃跑科蒂优化算法 (eCOA) 执行的集合模型的超参数优化.
主要成果:
- CDM-BEOSAEM方法在ToN-IoT数据集上表现出卓越的表现.
- 在网络攻击检测方面取得了99.29%的惊人的准确性,超过了现有的模型.
- 在现实世界物联网安全环境中验证了拟议的集体学习和优化技术的有效性.
结论:
- CDM-BEOSAEM方法为提高物联网环境中网络攻击检测提供了一个高度有效的解决方案.
- 整合BEOSA用于特征选择以及由eCOA优化的一组BiGRU,AE和GCN模型,显著提高了检测准确性.
- 这项研究为应对复杂的网络威胁提供了强大的防御机制,解决了物联网领域的关键安全挑战.
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