ρi-BLoM: a privacy preserving framework for the industrial IoT based on blockchain and machine learning

Abstract

The Industrial Internet of Things (IoT) comes together with different services, industrial applications, sensors, machines, and databases. Industrial IoT is improving the lives of the people in various ways such as smart cities, e-healthcare, and agriculture etc. Although Industrial IoT shares some characteristics with customer IoT, for both networks, separate cybersecurity techniques are used. Industrial IoT solutions are more likely to be incorporated into broader operational systems than customer IoT solutions, which are utilized by the single user for a particular purpose. As a result, Industrial IoT security solutions necessitate more preparation and awareness in order to ensure the system’s security and privacy. In this research paper, a random subspace and blockchain based technique is proposed. PCA is used as a preprocessing technique to preprocess the data. Furthermore, all the communication and node details are shared through blockchain to provide more secure communication. The integration of the blockchain in the existing approach gives better results in comparison to the other methods. The proposed methodology achieves better results in comparison to the previous techniques. The proposed methodology improves attack detection efficiency in comparison to the state-of-the-art machine learning techniques for IoT security.