In Mobile Edge Computing (MEC), efficiently scheduling limited resources to fulfill the service requirements of massive Internet of Things (IoT) ground devices (GDs) presents a significant challenge. Unmanned Aerial Vehicles (UAVs), as airborne mobile devices, can be deployed to GD Task Areas (TAs) to provide communication relay or computational support. In this paper, a new multiple UAVs scheduling problem with UAV heterogeneity and time windows constraints is considered and modeled as a Multi-Traveling Salesman Problem (MTSP) with soft time constraints. A two-stage heuristic Heterogeneous Multiple UAVs Routing (HMUR) was proposed for the problem. The approach first identifies TAs and the optimal hovering positions for UAVs, and defines an effective fitness measurement to match between UAVs and different routing path under certain energy constraints. A score function is also defined to determine the final path under the real-time constraints of tasks, thereby enhancing the Quality of Service (QoS). Simulation results demonstrate that our proposed HMUR method surpasses existing baseline algorithms across multiple metrics, validating its effectiveness in optimizing resource scheduling in MEC environments.

Abstract Intrusion detection systems are increasingly using machine learning. While machine learning has shown excellent performance in identifying malicious traffic, it may increase the risk of privacy leakage. This paper focuses on implementing a model stealing attack on intrusion detection systems. Existing model stealing attacks are hard to implement in practical network environments, as they either need private data of the victim dataset or frequent access to the victim model. In this paper, we propose a novel solution called Fast Model Stealing Attack (FMSA) to address the problem in the field of model stealing attacks. We also highlight the risks of using ML-NIDS in network security. First, meta-learning frameworks are introduced into the model stealing algorithm to clone the victim model in a black-box state. Then, the number of accesses to the target model is used as an optimization term, resulting in minimal queries to achieve model stealing. Finally, adversarial training is used to simulate the data distribution of the target model and achieve the recovery of privacy data. Through experiments on multiple public datasets, compared to existing state-of-the-art algorithms, FMSA reduces the number of accesses to the target model and improves the accuracy of the clone model on the test dataset to 88.9% and the similarity with the target model to 90.1%. We can demonstrate the successful execution of model stealing attacks on the ML-NIDS system even with protective measures in place to limit the number of anomalous queries.