Predicting the APT for Cyber Situation Comprehension in 5G-Enabled IoT Scenarios Based on Differentially Private Federated Learning

Abstract

Driven by the advancements in 5G-enabled Internet of Things (IoT) technologies, the IoT devices have shown an explosive growth trend with massive data generated at the edge of the network. However, IoT systems exhibit inherent vulnerability for diverse attacks, and Advanced Persistent Threat (APT) is one of the most powerful attack models that could lead to a significant privacy leakage of systems. Moreover, recent detection technologies can hardly meet the demands of effective security defense against APTs. To address the above problems, we propose an APT Prediction Method based on Differentially Private Federated Learning (APTPMFL) to predict the probability of subsequent APT attacks occurring in IoT systems. It is the first time to apply a federated learning mechanism for aggregating suspicious activities in the IoT systems, where the APT prediction phase does not need any correlation rules. Moreover, to achieve privacy-preserving property, we further adopt a differentially private data perturbation mechanism to add the Laplacian random noises to the IoT device training data features, so as to achieve the maximum protection of privacy data. We also present a 5G-enabled edge computing-based framework to train and deploy the model, which can alleviate the computing and communication overhead of the typical IoT systems. Our evaluation results show that APTPMFL can efficiently predict subsequent APT behaviors in the IoT system accurately and efficiently.