Enhancing IDS through Decentralization: A Study on Federated Learning and Blockchain Integration

These days cyberattacks are growing rapidly and becoming increasingly complex. Along with that data privacy is a very big concern. So, building systems powered by technologies like Machine Learning and Blockchain is crucial. There is a need to develop networks that can defend themselves, and keep the data confidential. To address this challenge, Federated Learning can be a powerful solution. This paper proposes a groundbreaking approach for setting up a intrusion detection system combining Federated Learning and Blockchain technology. Traditional intrusion detection systems have their limitations. They rely on pre-defined signatures to identify malicious activity. But these are vulnerable to attack. Federated Learning leverages large amounts of data distributed in the client devices and allows multiple clients to work hand-in-hand in order to train a Machine Learning model without the need to share sensitive data. Firstly, data collection occurs at various clients after which each client processes the data locally. Then various pre-processing and data standardization tasks are performed like feature extraction and normalization. Finally the updates are sent to a central system for aggregation. Now, a new version of global model is distributed back to all the client devices. In this project, we have also used blockchain technology which helps in enhancing security, trust, and reliability in the system. Continuous model updates are made possible without compromising system integrity. Finally, the decision is based on the network data, analyzed by the trained model, to determine if the intrusion is normal or an anomaly. We aim to build a system that is self-reliant in overcoming security threats while keeping data privacy in mind.