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先进的机器学习方法用于DoS攻击弹性在车辆互联网的安全性

Nadeem Ahmed¹, Fayaz Hassan¹, Khursheed Aurangzeb²

¹School of Electronic Science, Beijing University of Posts and Telecommunications, Beijing, 10086, China.

|April 29, 2024

概括

本研究介绍了一种用于车辆网络 (VANET) 的智能入侵检测系统 (IDS),使用机器学习 (ML) 打击拒绝服务 (DoS) 和分布式拒绝服务 (DDoS) 攻击,实现高精度.

科学领域:

车辆网络车辆网络
网络安全网络安全
机器学习机器学习

背景情况:

车载网络 (VANET) 面临着重大安全挑战,特别是拒绝服务 (DoS) 和分布式拒绝服务 (DDoS) 攻击.
这些攻击以恶意流量压倒网络资源,破坏网络资源,危及车辆通信安全.

研究的目的:

为VANET提出一个智能入侵检测系统 (IDS),利用机器学习 (ML) 来缓解DoS和DDoS攻击.
通过结合随机投影 (RP) 和随机矩阵分解 (RMF) 方法来增强IDS的检测能力.

主要方法:

利用公开可用的应用层数据集进行培训和验证.
实施了一种混合方法,将随机投影 (RP) 和随机矩阵分解 (RMF) 结合起来用于特征提取.
评估了多种机器学习模型,包括额外树分类器 (ETC),物流回归 (LR) 和随机森林 (RF).

主要成果:

拟议的基于ML的IDS实现了0.98.98的平均系统精度.
结合RP和RMF方法有效地从网络流量数据中提取了有意义的特征.
与现有方法相比,该系统在检测DoS和DDoS攻击方面表现出卓越的性能和效率.

结论:

开发的智能IDS显著提高了对DoS和DDoS攻击的车辆网络安全性.
该研究提供了一种强大而高效的解决方案,以确保连接车辆的通信.
这些发现为未来在保护互联汽车不断变化的环境方面取得的进展奠定了基础.

关键词:

这是一次DDoS攻击.机器学习机器学习安全的安全的安全的安全的安全.车辆网络车辆网络

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