Go To Anywhere: A Multi-Armed Bandit Based Offloading Attack in Edge Computing

Task scheduling is a critical component in edge computing. Many advanced scheduling strategies select the most suitable execution node based on user-reported resource requirements, thereby enhancing user experience. However, the reliance on user-reported resource demands presents a potential vulnerability, as malicious users could exploit this to disrupt edge servers. In this paper, we propose a hypothetical offloading attack scenario in which an attacker submits tasks with falsified resource demands, directing these tasks to specific nodes. A high volume of malicious tasks leads to congestion at the targeted node, causing delays and potential failures for latency-sensitive tasks. Due to the black-box nature of the offloading strategy and the unknown system environment, we model the task parameter manipulation as a multi-dimensional multi-armed bandit (MAB) process with non-stationary rewards. This approach aims to maximize the attack efficiency within limited attack costs. We introduce UCB algorithm for Multidimensional space with Cost constraints and Non-stationary rewards(UCB-MCN), an extended MAB algorithm designed to optimize the attacker's cumulative reward. In a simulated environment, UCB-MCN is tested against various offloading strategies and defense mechanisms. Evaluation results demonstrate the effectiveness of UCB-MCN and highlight new security concerns within edge computing.