车载资源约束下的控制器域网络异常检测自适应优化方法

Adaptive Optimization Method for Controller Area Network Anomaly Detection under Vehicle Resource Constraints

针对在有限的车载资源约束条件下,如何兼顾控制器域网络(CAN)异常检测准确度和时效性的问题,该文提出一种CAN网络异常检测自适应优化方法。首先,基于信息熵建立了CAN网络异常检测的准确度和时效性量化指标,并将CAN网络异常检测建模为多目标优化问题;然后,设计了求解多目标优化问题的第二代非支配排序遗传算法(NSGA-II),将帕累托前沿作为CAN网络异常检测模型参数的优化调整空间,提出了满足不同场景需求的检测模型鲁棒控制机制。通过实验分析,深入剖析了优化参数对异常检测的影响,验证了所提方法能够在有限车载资源下适应多样化检测场景需求。

Considering the problem of how to take into account the accuracy and timeliness of Controller Area Network(CAN)anomaly detection under the constraints of limited vehicle resources,an adaptive optimization method for CAN anomaly detection is proposed.Firstly,based on information entropy,the quantification index of the accuracy and timeliness of CAN network anomaly detection is established,and the CAN anomaly detection is modeled as a multi-objective optimization problem.Then,the Non-dominated Sorting Genetic Algorithm-II(NSGA-II)algorithm for solving the multi-objective optimization problem is designed.The Pareto frontier is used as the optimization and adjustment space of the parameters of the CAN anomaly detection model,and a robust control mechanism of the detection model is proposed to meet the needs of different scenarios.Through experimental analysis,the influence of optimization parameters on anomaly detection is deeply analyzed,and it is verified that the proposed method can adapt to the needs of diverse detection scenarios under limited vehicle resources.