| A key issue when operating an Internet of things (IoT) network is that devices may be infected by malware. Consequently, when collecting data from these devices, e.g., using an unmanned aerial vehicle (UAV), malware data may be transmitted to a gateway that is then used to attack servers. To address this issue, we equip a UAV with a virtual network function, and study a novel problem involving the allocation of computation and communication resource to identify malware traffic from solar-powered devices. To solve this problem, we first outline a Mixed Integer Linear Program (MILP) that jointly optimizes (i) UAV placement, (ii) UAV trajectory, (iii) data processing, (iv) channel allocation, and (v) energy usage of devices. To facilitate online decision making, we design a neural network-based solution called Neural Network Mapping (NNM) to store integer valued decision variables offline. During flight, the UAV then uses the neural network to retrieve the corresponding integer values for a given scenario, which allows the UAV to solve the said MILP as a linear program that can be computed quickly. Our simulation results show that NNM is able to effectively reduce the amount of data from malware that arrives at a gateway. |