针对基于SVM的GPS伪造检测系统的对抗性逃避攻击
Sunghyeon An1, Dong Joon Jang1, Eun-Kyu Lee1
1Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Republic of Korea.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
智能对手可以利用基于机器学习的GPS伪造检测系统中的漏洞. 新的逃避策略在绕过这些防御方面取得了显著的成功,突出显示了自动驾驶汽车需要提高对抗性强度的需求.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 全球定位系统 (GPS) 伪造对自动驾驶汽车构成重大威胁.
- 机器学习模型,如支持向量机 (SVM),通常用于GPS伪造检测.
- 这些检测系统对复杂的敌对攻击的稳定性尚未得到充分理解.
研究的目的:
- 调查基于SVM的GPS伪造检测模型对智能对手的脆弱性.
- 为敌对GPS信号开发新的逃避策略.
- 评估这些策略在绕过SVM探测器方面的有效性.
主要方法:
- 基于SVM的GPS伪造检测模型的决策边界的分析.
- 开发数据位置转移攻击和基于相似性的噪音攻击.
- 在CARLA自动驾驶环境中模拟逃避策略.
主要成果:
- 一个适度的位置转移攻击将检测准确度从99.9%降低到20.4%.
- 基于相似性的噪音攻击在很大程度上逃避了检测,同时降低了性能.
- 在相似性和转移之间观察到非线性取消效应,表明可检测性-影响权衡.
结论:
- 基于SVM的GPS伪造检测系统容易受到新型逃避攻击.
- 现有的防御可能无法对抗聪明的对手.
- 增强的对抗性稳定性对于基于机器学习的车辆系统中的伪造检测至关重要.
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