Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems

Abstract

With the proliferation of IoT devices, ensuring the security and privacy of these devices and their associated data has become a critical challenge. In this paper, we propose a federated sampling and lightweight intrusion-detection system for IoT networks that use K-meansfor sampling network traffic and identifying anomalies in a semi-supervised way. The system is designed to preserve data privacy by performing local clustering on each device and sharing only summary statistics with a central aggregator. The proposed system is particularly suitable for resource-constrained IoT devices such as sensors with limited computational and storage capabilities. We evaluate the system’s performance using the publicly available NSL-KDD dataset. Our experiments and simulations demonstrate the effectiveness and efficiency of the proposed intrusion-detection system, highlighting the trade-offs between precision and recall when sharing statistics between workers and the coordinator. Notably, our experiments show that the proposed federated IDS can increase the true-positive rate up to 10% when the workers and the coordinator collaborate.

Review on Approaches of Federated Modeling in Anomaly-Based Intrusion Detection for IoT Devices

“…There are some important datasets are used AIDS model evaluation for IoT device. The Unisa Malware Dataset (UMD) was utilized by [110], while the CICIDS2017 dataset found attention from [81,111]. Multiple authors were involved in studying the TON_IoT dataset [55,83,84,90,93,94,112], while on N-BaIoT dataset [112], MNIST dataset [91], Edge-IIoTset dataset [77,92], CIC_IoT dataset [80], CSE-CIC-IDS dataset [113].…”

“…Toy classifiers [90], serving as training controls in this classification models, offer rudimentary decision rules, laying the groundwork for comprehending more algorithms that are intricate. Finally, in one-class SVMs stand as unsupervised approach [111], vigilantly guarding the borders of normalcy within the data. This unshackled from the constraints of labeled anomalies, they excel at identifying outliers that deviate from the established patterns, serving as invaluable tools for anomaly detection.…”