Trust-Based Consensus and ABAC for Blockchain Using Deep Learning to Secure Internet of Things

The rapid increase in Internet of Things (IoT) applications has exposed critical security vulnerabilities, particularly concerning user privacy and identity forgery. To address these concerns, Blockchain technology offers a promising solution by providing strong security and ensuring data integrity through its transparent ledger system. By leveraging blockchain, IoT systems can enhance their security protocols, making it more difficult for attackers to exploit vulnerabilities and access sensitive data. We proposed Attribute-Based Access Control (ABAC) integrated with trust-based delegated consensus blockchain (TDCB) technology. The ABAC scheme employs Fully Homomorphic Encryption (FHE) processes to encrypt attributes and access regulations, enabling analytical operations directly on encrypted data. Dueling Double Deep Q-Networks with Prioritized Experience Replay (D3P) with Deep Reinforcement Learning (DRL) collaborate with Multiple blockchain nodes to decode the ABAC system’s data and optimize the performances of the blockchain. Our proposed scheme ABAC-TDBC-D3P enhances throughput and security and reduces total computing costs. The simulation results demonstrate that the suggested ABAC-TDCB-D3P scheme has a percentage of 86% for Collusive Rumour Attack (CRA) and 91% for Naive Malicious Attack (NMA). Significant improvements in blockchain security, particularly in mitigating the impact of malicious nodes, were compared to previous schemes

A Detailed Review on Enhancing the Security in Internet of Things-Based Smart City Environment Using Machine Learning Algorithms

Over the past few years, smart cities have seamlessly integrated into our daily lives, offering convenience and simplicity. However, as these cities become increasingly interconnected and reliant on the Internet of Things (IoT), ensuring heightened security measures becomes paramount. The potential compromise of IoT devices due to vulnerabilities poses significant risks, including the theft of personal data, leading to severe hazards for individuals. Thus, Security plays a pivotal role in safeguarding IoT devices. In this modern era, integrating security measures with machine learning has emerged as a solution to automate and streamline security protocols. This requires a comprehensive analysis of enhancing security levels in IoT devices within innovative city environments. Our study extensively surveys security issues across various facets of IoT infrastructure, including hardware, cloud environments, applications, data, software, and networks. Through thorough examination, we identify the effects of these issues and propose countermeasures to bolster Security, mainly focusing on IoT devices. Furthermore, our study delves into various machine learning algorithms, providing examples, detailing attack types, and assessing accuracy rates for each algorithm. We offer a quick reference guide that outlines the benefits and drawbacks of different machine-learning algorithms and their applications. Additionally, we aim to identify and mitigate various security threats by exploring diverse datasets, evaluation metrics, IoT threats, and machine-learning techniques. By thoroughly exploring these aspects, our study equips future researchers with the knowledge to effectively identify potential security threats and implement robust safeguards against them