车辆互联网中的入侵检测系统的多分类和基于树的整体网络
Wanting Gou1, Haodi Zhang1, Ronghui Zhang2
1China Telecom Research Institute, Guangzhou 510630, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
本研究介绍了用于车辆互联网 (IoV) 的先进入侵检测系统 (IDS),以打击网络攻击. 这种新型系统有效地处理不平衡的数据和各种威胁,实现高检测准确度,以确保安全的车辆通信.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 侵入检测系统 侵入检测系统
背景情况:
- 车辆互联网 (IoV) 依赖于车辆到一切 (V2X) 技术,创建易受网络攻击的复杂网络.
- 现有的入侵检测系统 (IDS) 面临不平衡的网络流量数据和多类网络攻击的多样性.
- 确保安全的车载和车辆间通信对于IoV的功能和安全至关重要.
研究的目的:
- 为汽车互联网 (IoV) 开发一个强大的入侵检测系统 (IDS),能够解决数据失衡和多类攻击检测.
- 加强车辆通信网络的安全性,以应对广泛的网络威胁.
- 提高IoV环境中入侵检测的效率和准确性.
主要方法:
- 采用合成少数超样本技术 (SMOTE) 和RandomUnderSampler实施了一种混合数据平衡技术.
- 提出了基于树的自适应组合网络,具有深层结构,用于准确的网络攻击的多类分类.
- 利用机器学习进行特征选择,以减少维度和计算开销.
主要成果:
- 在CICIDS2017数据集上获得0.965的F1得分,在汽车黑客数据集上获得0.9999的F1得分,表现出高性能.
- 拟议的IDS有效地对各种网络攻击进行了准确和高效的多类分类.
- 该系统成功地缓解了IoV网络中数据不平衡和复杂攻击载体带来的挑战.
结论:
- 开发的IDS在保护IoV通信免受复杂的网络威胁方面取得了重大进展.
- 基于树的自适应组合方法为车辆网络中的多类入侵检测提供了强大的解决方案.
- 这项研究为提高汽车互联网的网络安全态度提供了有价值的参考.
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