The rapid advancement of autonomous driving technologies has imposed stringent requirements on low-latency and high-reliability computation, which often exceed the capabilities of onboard processors. Vehicular edge computing (VEC) provides a promising solution by offloading computation to external servers; however, terrestrial infrastructure suffers from fragmented coverage and limited scalability, particularly in highway and rural scenarios. To address these limitations, this paper considers a multi-layer aerial VEC network integrating a high-altitude platform and multiple unmanned aerial vehicles (UAVs) to jointly provide wide-area coverage and proximity services. Different from existing works that primarily focus on latency minimization under homogeneous resources, this paper explicitly models the heterogeneous leasing pricing of aerial platforms and investigates its impact on task offloading decisions. A joint task offloading and resource allocation problem is formulated to minimize the total system cost, defined as a weighted combination of latency and economic expenditure. To ensure the feasibility of UAV-assisted offloading under high mobility, a dwell-time constraint is incorporated to restrict task execution within the effective service duration. The resulting problem is formulated as a mixed-integer nonlinear programming problem, which is solved via a low-complexity iterative algorithm based on Lagrangian duality, linear relaxation, and the alternating direction method of multipliers. Simulation results demonstrate that the proposed scheme achieves significant cost reduction compared with benchmark strategies, especially under high-mobility conditions.

Machine learning technology has achieved excellent results in Android malware detection, however, existing detection methods ignore the frequent changes of API in malware, resulting in their detection performance continuing to decline over time. In this paper, we propose an evolved Android malware detection method (EAMDM). Two components comprise EAMDM: API clustering and malware detection. Before malware detection, we perform API clustering to obtain cluster centers representing the function of each API. we employ Bert to comprehensively extract the semantic information contained in API features such as method name, exception, and permission. Bert generates feature vectors for clustering that represent the similarity of API functions. In malware detection, EAMDM abstracts the API into cluster centers in order to maintain resilience against the frequent changes of API in both malware and Android framework. We evaluate the effectiveness of EAMDM on a dataset of 85K apps developed over seven years. The experimental results show that EAMDM greatly outperforms the existing classic methods and has a significantly slower aging speed.