Enhancing Cloud and IoT Security Using Deep Learning-Based Intrusion Detection Systems with Blockchain and Federated Learning

Abstract

This paper proposes a new model that sits in the domain of improving security mechanisms in cloud and Internet of Things (IoT) utilizing deep learning based intrusion detection systems (IDS) with a sleeping stack technological, federated learning and blockchain technology. This theoretical framework seeks to address the urgent issue of protecting decentralized systems against advanced cyber threats, upholding data integrity, and maintaining privacy. Based on deep learning algorithms, the IDS detects and classifies possible security threats on a distributed network efficiently. Blockchain is used to create an immutable, transparent record of identified threats, offering solid forensic evidence and supporting decentralized, tamper-proof security protocols. In addition, federated learning is utilized in the context of our IDS models training over distributed edge nodes such that sensitive information is never shared while the models are trained, which preserves privacy per modern data protection requirements. Therefore, Preliminary experimental results show that our performance increases compared to various machine learning algorithms, as we also improve the speed of analysis, which is crucial for the intrusion detection system to react timely and minimize damage. Furthermore, the combination of blockchain and federated learning leads to improved scalability, reduced latency, and increased robustness of defense mechanisms. We demonstrate that, together the synergy of the two technologies provides a robust, scalable and privacy-respecting solution to meet the security needs of today's distributed IoT and cloud systems.