Leveraging Blockchain and Machine Learning to Improve IoT Security for Smart Cities

Abstract

Recently, Internet of Things (IoT) infrastructures are developing various applications in sustainable smart cities and societies. However, there are numerous challenges in smart cities, such as security, privacy, trust, verifiability, communication latency, scalability, and centralization preventing faster adaptations of IoT. Machine Learning (ML) is an important analytic tool that provides a scalable and accurate analysis of data in real time. However, there are several obstacles to designing and developing a usable large data analysis tool utilizing ML, such as centralized architecture, security, and privacy, resource limits, and a lack of sufficient training data. Blockchain, as opposed to that, promotes a decentralized architecture as new technology. It encourages the secure sharing of data, and resources among the various nodes of the IoT network, removing centralized control and overcoming ML’s current difficulties. As a result, this study provides a smart city intrusion detection system. This system consists of three modules: a trust module based on designing an address-based blockchain reputation system, a two-level privacy module based on blockchain-based enhanced Proof of Work technique, and an intrusion detection module. We provide a blockchain-IPFS integrated Edge-Fog-Cloud infrastructure, named Cloud-Block, Fog-Block, and Edge-Block, to utilize the system proposed for smart cities, related to the inherited strengths and shortcomings of Edge-Fog-Cloud architecture. The sBoT-IoT and TON-IoT datasets are utilized to evaluate the system. Finally, a comparison of our implementation results shows that our system outperforms other state-of-the-art systems.