自动驾驶汽车网络攻击模拟与防御 -安全工程专业

摘要

随着自动驾驶技术的快速发展，其网络安全性问题日益凸显，成为制约该领域进一步发展的关键因素之一。本研究旨在通过构建自动驾驶汽车网络攻击模拟平台，深入分析潜在威胁及其传播机制，并提出针对性防御策略。研究采用多层仿真框架，结合实际交通场景与网络安全模型，系统性评估了多种典型攻击方式（如欺骗攻击、拒绝服务攻击等）对自动驾驶系统的性能影响。同时，基于机器学习算法开发了一种实时入侵检测系统，显著提升了异常行为识别的准确率和效率。实验结果表明，所提出的防御方案能够在复杂动态环境中有效降低攻击成功率，保障车辆通信及控制系统的稳定性。本研究的主要创新点在于将网络安全理论与自动驾驶技术深度融合，首次提出了适用于车载网络环境的自适应防御机制，并通过大规模仿真实验验证了其可行性和优越性。研究成果为未来自动驾驶系统的安全设计提供了重要参考，也为相关标准制定奠定了理论基础。

关键词：自动驾驶安全性；网络攻击模拟；自适应防御机制；实时入侵检测；机器学习算法

ABSTRACT

With the rapid development of autonomous driving technology, its network security issues have become increasingly prominent, becoming one of the key factors constraining further progress in this field. This study aims to construct a network attack simulation platform for autonomous vehicles to conduct an in-depth analysis of potential threats and their propagation mechanisms, while proposing targeted defense strategies. A multi-layer simulation fr amework is employed, integrating real-world traffic scenarios with cybersecurity models, to systematically evaluate the performance impacts of various typical attack methods, such as spoofing attacks and denial-of-service attacks, on autonomous driving systems. Meanwhile, a real-time intrusion detection system based on machine learning algorithms has been developed, significantly enhancing the accuracy and efficiency of anomaly behavior identification. Experimental results demonstrate that the proposed defense scheme can effectively reduce attack success rates and ensure the stability of vehicle communication and control systems in complex dynamic environments. The primary innovation of this research lies in the deep integration of cybersecurity theory with autonomous driving technology, where an adaptive defense mechanism tailored for in-vehicle network environments is proposed for the first time. Its feasibility and superiority are validated through large-scale simulation experiments. The research findings provide critical references for the secure design of future autonomous driving systems and lay a theoretical foundation for the formulation of relevant standards.

Keywords: Autonomous Driving Safety; Network Attack Simulation; Adaptive Defense Mechanism; Real-Time Intrusion Detection; Machine Learning Algorithm

摘要 I

ABSTRACT II

第1章绪论 1

1.1 自动驾驶汽车网络攻击的研究背景 1

1.2 网络攻击模拟与防御的意义分析 1

1.3 当前研究现状与技术挑战 2

第2章自动驾驶汽车网络攻击类型分析 3

2.1 常见网络攻击方式概述 3

2.2 针对传感器的攻击行为剖析 3

2.3 车载通信系统的漏洞研究 4

2.4 攻击场景的实际案例分析 4