A Machine Learning-Powered Solution for Safe Autonomous Robotic Ground Navigation in Cyber-Contested Environments

Engineering notes

Then, a dataset comprising these samples and other injected samples that simulate two cyberattacks, namely path modification (PM) and velocity drift (VD), is created to train, validate, and benchmark various ML classification models.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

In this article, machine learning (ML) is proposed as a solution to detect and classify false message injection attacks in autonomous ground navigation. First, multiple trajectories are designed and simulated to collect authentic feature samples offered by the odometry and inertial measurement unit (IMU) of an autonomous ground vehicle (UGV). Then, a dataset comprising these samples and other injected samples that simulate two cyberattacks, namely path modification (PM) and velocity drift (VD), is created to train, validate, and benchmark various ML classification models. These include decision tree (DT), k-nearest neighbors (KNN), multi-layer perceptron (MLP), random forest (RF), and support vector machine (SVM). The optimum classification model is experimentally evaluated using a UGV platform, and results suggest that the proposed solution allows the detection of authentic and attacked messages with more than 98% average accuracy and sub-millisecond prediction time. Thus, this solution is ideal for real-time classification, especially in fixed-route applications, e.g., public transportation.

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