Starlet: Network defense resource allocation with multi-armed bandits for cloud-edge crowd sensing in IoT

Abstract

The cloud platform has limited defense resources to fully protect the edge servers used to process crowd sensing data in Internet of Things. To guarantee the network's overall security, we present a network defense resource allocation with multi-armed bandits to maximize the network's overall benefit. Firstly, we propose the method for dynamic setting of node defense resource thresholds to obtain the defender (attacker) benefit function of edge servers (nodes) and distribution. Secondly, we design a defense resource sharing mechanism for neighboring nodes to obtain the defense capability of nodes. Subsequently, we use the decomposability and Lipschitz continuity of the defender's total expected utility to reduce the difference between the utility's discrete and continuous arms and analyze the difference theoretically. Finally, experimental results show that the method maximizes the defender's total expected utility and reduces the difference between the discrete and continuous arms of the utility.