用自适应DBSCAN实时检测自动驾驶车辆的GPS欺骗攻击
GPS Spoofing Attack Detection in Autonomous Vehicles Using Adaptive DBSCAN
- 动态调整聚类阈值ε，基于正常数据的位移误差均值与方差递推更新
- 在本田研究机构数据集上对多种欺骗攻击的检测准确率超98%
- 适合关注车载安全与抗干扰能力的自动驾驶系统开发者
随着自动驾驶车辆成为现代交通的重要组成部分，其面临诸如GPS欺骗攻击等安全威胁的风险日益增加。本文提出一种基于自适应密度聚类（DBSCAN）的检测方法，通过实时动态调整聚类半径ε，实现对异常行为的灵敏识别。该阈值根据非异常时刻的GPS与车载传感器间位移误差的递推均值和标准差进行更新，并利用12万条干净数据样本预设初始阈值，确保从初期即可发现细微且渐进式的欺骗攻击。在真实世界本田研究院驾驶数据集（HDD）的五个子集上，模拟了大、小幅度的欺骗攻击。所提方法有效识别出逐段转向、停车、越界及多次小偏移等攻击类型，检测准确率分别为98.621%、99.960.1%、99.880.1%和98.380.1%。该研究显著提升了自动驾驶系统应对GPS欺骗攻击的安全性与可靠性。
原文摘要 · Abstract (English)
As autonomous vehicles become an essential component of modern transportation, they are increasingly vulnerable to threats such as GPS spoofing attacks. This study presents an adaptive detection approach utilizing a dynamically tuned Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, designed to adjust the detection threshold (ε) in real-time. The threshold is updated based on the recursive mean and standard deviation of displacement errors between GPS and in-vehicle sensors data, but only at instances classified as non-anomalous. Furthermore, an initial threshold, determined from 120,000 clean data samples, ensures the capability to identify even subtle and gradual GPS spoofing attempts from the beginning. To assess the performance of the proposed method, five different subsets from the real-world Honda Research Institute Driving Dataset (HDD) are selected to simulate both large and small magnitude GPS spoofing attacks. The modified algorithm effectively identifies turn-by-turn, stop, overshoot, and multiple small biased spoofing attacks, achieving detection accuracies of 98.621%, 99.960.1%, 99.880.1%, and 98.380.1%, respectively. This work provides a substantial advancement in enhancing the security and safety of AVs against GPS spoofing threats.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。