A review paper: Blockchain security with IoT devices and deep-learning methods

Abstract

Internet of Things (IoT) is changing the face of modern world by interconnecting billions of devices, transforming human life and revolutionizing innovation in various domains such as healthcare, transportation, and smart cities. However, the widespread deployment of IoT applications is dramatically hindered by the aforementioned remaining challenges, which mostly relate to the security, privacy, and trustworthiness of data. This work also discusses their limitations including: small computing power, use of insecure communication protocols, weak authentication, lack of encryption and susceptibility to Distributed Denial of Services (DDoS) attacks. Blockchains A blockchain is a decentralized, tamper-resistant authority which exhibits high levels of security, which could help solve the mentioned security problems of the IoT ecosystems. Blockchain can provide trust through data immutability, transparency and consensus and hence can be employed to provide security and reliability to IoT communication and storage. But integrating blockchain directly in low-resource IoT systems has certain issues like added latency, increased bandwidth usage and additional computational overhead. The objective of this paper is to present a detailed review of the synergy between the architecture of blockchain, the IoT systems and deep learning techniques, and the way that they can be collaboratively utilized for improving data security, trustworthiness, and decision-making in IoT networks. Specifically, we examine existing blockchain-based deep learning frameworks by categorizing them based on four major criteria: blockchain type (public, private, consortium), deep learning approach (e.g., CNN, GAN, DRL), consensus protocols, and datasets used for model training and validation. Furthermore, the paper analyzes the strengths and limitations of these integrated frameworks in addressing real-world IoT challenges. The review highlights how blockchain enhances the trustworthiness and traceability of deep learning outputs, while deep learning models can contribute to intelligent threat detection, adaptive control, and data-driven automation in IoT networks. Finally, we outline current research gaps and propose future research directions that focus on optimizing blockchain-deep learning frameworks for scalability, energy efficiency, and real-time performance in large-scale IoT deployments.