Privacy in federated learning models for intrusion detection systems

Abstract

Federated learning-based solutions enable multiple organizations to build a machine learning model using the collective knowledge of the organizations without exchanging the sensitive raw data of any participant. The key roadblock for widespread adoption is the possibility of training data leakage in federated learning models, which have been reported in federated learning models for image processing, natural language processing, and tabular data. However, the impact and feasibility of such privacy leakage attacks on federated learning-based intrusion detection systems (IDS) remains largely unexplored. In this work, for the first time, we focus on the problem of training data privacy in federated learning-based network IDS by exploring the implementations, techniques, and limitations of existing federated IDS solutions. First, we provide a systematic study of the landscape of machine learning-based network IDS and federated learning-based network IDS. Second, we demonstrate that data leakage attacks on federated learning-based IDS are yet to be explored to the full extent by the research community. Finally, we describe the existing defenses against such attacks and suggest the possibility of using these approaches for federated network IDS in the future.