Robust authenticated attribute-dependent security feature for cloud-assisted smart industrial control systems

Smart industries are dependent on recent communication trends for leveraging the production insights through automation and controlled functions. The communication technology automates the processes of the different layers of the industry to improve the performance and optimization of the production/manufacturing. Cloud-based industry automation is common for providing analytical and security solutions augmenting the optimization process. In this manuscript, Attribute-dependent Security Feature (ASF) for smart Industrial Control Systems (ICSs) is introduced. This security feature is capable of simplifying complex big data from the industrial environment for providing narrow authentication. This feature first classifies the data based on time and attribute for which a less computation requiring robust authentication is provided. By regression learning, the complex data is classified and using the attributes, security measures are formulated. The proposed security feature is capable of reducing computation time and compromising, and improving the data reception ratio for the different data attributes and packets at varying sensing intervals.