在IoT/IIoT系统和自动驾驶汽车的软件中使用元启发式优化进行入侵检测
Pavle Dakic1,2, Miodrag Zivkovic1, Luka Jovanovic1
1Faculty of Informatics and Computing, Singidunum University, Belgrade, 11000, Serbia.
Scientific reports
|October 2, 2024
概括
机器学习分类器有效地检测汽车控制器区域网络 (CAN) 系统中的网络威胁. 优化的算法实现了超过89%的准确性,提高了车辆的网络安全和安全.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 汽车工程 汽车工程
背景情况:
- 越来越多的物联网 (IoT) 系统在车辆中的集成引入了重大的网络安全风险.
- 汽车控制器区域网络 (CAN) 系统中断可能导致严重故障,伤害和死亡.
- 确保车辆的安全和保障至关重要,尤其是在实现自动驾驶的过程中.
研究的目的:
- 调查机器学习分类器在汽车CAN系统中检测网络入侵的有效性.
- 通过采用数据驱动技术,应对不同制造商标准的挑战.
- 改善互联汽车的整体网络安全态度.
主要方法:
- 实施和评估极端梯度提升和K-最近邻居机器学习分类器.
- 开发一个修改后的元启发式优化器,用于对分类器进行高效的参数选择.
- 在公开可用的汽车CAN数据集上测试拟议的方法.
主要成果:
- 性能最好的机器学习模型在检测网络攻击时的准确性超过89%.
- 统计分析证实了优化器结果的稳定性.
- 使用可解释的AI技术来识别影响模型性能的关键特征.
结论:
- 机器学习,特别是具有优化的参数,为检测CAN系统中的网络威胁提供了可行的解决方案.
- 拟议的数据驱动方法提高了汽车网络对不断变化的网络威胁的安全性.
- 这项研究有助于提高自动驾驶时代的车辆安全性和可靠性.
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