XAI FL-IDS: A Federated Learning and SHAP-Based Explainable Framework for Distributed Intrusion Detection Systems
It addresses privacy and explainability limitations in centralized IDS for IoT networks, but the approach is incremental.
The paper proposes XAI FL-IDS, a federated learning framework with SHAP-based explainability for distributed intrusion detection, achieving over 99% accuracy on the Edge-IIoTset dataset.
An Intrusion Detection System (IDS) is vital in cybersecurity, detecting unauthorized activity across networks. With attacks on network layers increasing, stronger IDSs are needed. Yet most IDSs rely on centralized detection, forcing IoT nodes to ship data to a server, adding overhead and offering no privacy guarantees. Moreover, conventional models focus solely on flagging attacks, without explaining how individual features influence those decisions. This research aims to address these dual limitations by first proposing a solution for privacy preservation and then adding explainability to the new system. We introduce an innovative framework called XAI FL-IDS, which integrates Federated Learning (FL) with Explainable AI (XAI). The XAI FL-IDS system eliminates concerns over data transfer because each node trains its data locally and only sends the necessary update parameters to the server. Additionally, all detections, both at the local node and central server levels, are scrutinized using SHapley Additive exPlanations (SHAP), providing detailed insight into the decision-making process. This system consists of a central server and 10 clients and utilizes the Edge-IIoTset dataset, which is distributed among all clients with careful attention paid to class balancing. On each client, the XGBoost model is executed on local data. The proposed method demonstrates robust efficiency and strong performance in intrusion detection, achieving an accuracy of over 99% and, at times, reaching 100%. By incorporating FL, the confidentiality of the network information on every local node is guaranteed.