QCAE-QOC-SVM:一种混合量子机器学习模型,用于对自动驾驶汽车CAN总线上的DoS和模糊攻击检测
Meghana R1, Sowmyashree Sakrepatna Ramesha1, Adwitiya Mukhopadhyay1
1Department of Computer Science, Amrita School of Computing, Amrita Vishwa Vidyapeetham Mysuru Campus, Karnataka, India.
MethodsX
|July 21, 2025
概括
本研究介绍了一种混合量子机器学习模型,用于检测自动驾驶汽车网络上的网络攻击. 量子卷积自编码器和量子直角分类器获得了99.43%的F1得分,超过了传统方法.
科学领域:
- 网络安全 网络安全
- 量子计算是一种量子计算.
- 机器学习 机器学习
背景情况:
- 自动驾驶汽车依赖控制器区域网络 (CAN) 总线进行通信.
- CAN总线系统容易受到网络攻击,例如拒绝服务 (DoS) 和模糊攻击.
- 现有的安全措施难以检测复杂和新型威胁.
研究的目的:
- 引入一种新的混合量子机器学习 (QML) 模型,用于对自动驾驶汽车 CAN 总线进行增强的网络攻击检测.
- 评估拟议的QML模型与传统机器学习 (ML) 和深度学习 (DL) 方法的性能.
- 展示QML在加强智能运输系统以应对不断变化的网络威胁方面的潜力.
主要方法:
- 开发了一种混合QML模型,将量子卷积自编码器 (QCAE) 和基于支持向量机器 (QOC-SVM) 的量子直角分类器结合起来.
- 在使用CARLA模拟器生成的来自公共和定制来源的30万个实例数据集上训练和验证模型.
- 在高性能计算设施上使用包括F1分数在内的指标评估模型性能.
主要成果:
- QCAE-QOC-SVM模型获得了99.43%的优异F1得分,超过了现有的ML,DL和其他QML模型.
- 该模型在检测正常信号,DoS和模糊攻击方面表现出高精度和耐力.
- 在7741:31.1的批量对批量大小比率下,性能得到了优化.
结论:
- 拟议的混合QML模型为自动驾驶汽车和智能交通系统的网络安全提供了显著的进步.
- 量子机器学习显示出开发强大的防御机制来应对复杂的网络攻击的巨大潜力.
- 这些发现支持开发面向未来的网络安全解决方案,以快速推进自动驾驶汽车技术.
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