A Novel Federated Edge Learning Approach for Detecting Cyberattacks in IoT Infrastructures

Abstract

The advancement of the communications system has resulted in the rise of the Internet of Things (IoT), which has increased the importance of cybersecurity research. IoT, which incorporates a range of devices into networks to offer complex and intelligent services, must maintain user privacy and deal with attacks such as spoofing, denial of service (DoS), jamming, and eavesdropping. Attacks change with time, and new ones develop every day. Numerous researchers look into IoT system attack models and evaluate machine, deep, and federated learning-based IoT security approaches. However, existing methods do not produce reliable and encouraging performance. Therefore, this study proposes a novel approach for leveraging federated learning to identify large attacks on IoT devices using the novel CIC_IoT 2023 dataset. The approach uses a federated deep neural network to achieve precise categorization. Before model training, the data was preprocessed using various data preparation techniques to guarantee the creation of a trustworthy dataset for categorization. The suggested approach involves feature normalization, data balancing, and model prediction utilizing federated learning. The experimental findings show that the proposed approach attained an exceptional accuracy of 99.00%, endorsing it for attack detection.

Keywords

• Internet of Things (IoT)
• networks attacks
• privacy
• preservational deep learning
• federated learning