Securing IoT Environment by Deploying Federated Deep Learning Models

The vast network of interconnected devices, known as the Internet of Things (IoT), produces significant volumes of data and is vulnerable to security threats. The proliferation of IoT protocols has resulted in numerous zero-day attacks, which traditional machine learning systems struggle to detect due to IoT networks' complexity and the sheer volume of these attacks. This situation highlights the urgent need for developing more advanced and effective attack detection methods to address the growing security challenges in IoT environments. In this research, we propose an attack detection mechanism based on deep learning for federated learning in IoT. Specifically, we aim to detect and prevent malicious attacks in the form of model poisoning and Byzantine attacks that can compromise the accuracy and integrity of the trained model. The objective is to compare the performance of a distributed attack detection system using a DL model against a centralized detection system that uses shallow machine learning models. The proposed approach uses a distributed attack detection system that consists of multiple nodes, each with its own DL model for detecting attacks. The DL model is trained using a large dataset of network traffic to learn high-level features that can distinguish between normal and malicious traffic. The distributed system allows for efficient and scalable detection of attacks in a federated learning network within the IoT. The experiments show that the distributed attack detection system using DL outperforms centralized detection systems that use shallow machine learning models. The proposed approach has the potential to improve the security of the IoT by detecting attacks more effectively than traditional machine learning systems. However, there are limitations to the approach, such as the need for a large dataset for training the DL model and the computational resources required for the distributed system.