Intelligent Cache Pollution Attacks Detection for Edge Computing Enabled Mobile Social Networks

With the rapid advances of wireless technologies and popularization of smart mobile devices, edge-enabled mobile social networks (MSNs) have emerged as a promising network paradigm for mobile users to deliver, share, and exchange contents with each other. By leveraging edge caching technology, various content services can be provided to mobile users for improving their quality of experience (QoE). However, edge caching is vulnerable to cache pollution attacks (CPAttacks) with the result of disruptive content delivery. To tackle this problem, we propose a hidden Markov model (HMM) based CPAttack detection scheme in edge-enabled MSNs. Specifically, we first present the CPAttack model based on observations of attacking behaviors. According to the CPAttack model, the caching state of the edge device is characterized by two parameters-content request rate and cache missing rate. Then, with observation sequence constructed by caching states, we develop an HMM-based detection algorithm to distinguish the CPAttack in the approximately time-invariant content request process. To deal with the lack of training data and dynamic of caching states, an adaptive HMM (AHMM) based algorithm is designed to detect the CPAttack in the time-varying content request process. The simulation results demonstrate that the proposed scheme can efficiently improve edge devices' abilities to sense the CPAttack.