LAUSR

Industry 4.0 represents the fourth phase of industry and manufacturing revolution, unique in that it provides Internet-connected smart systems, including automated factories, organizations, development on demand, and ‘just-in-time’ development. Industry 4.0 includes the integration of cyber-physical systems (CPSs), Internet of Things (IoT), cloud and fog computing paradigms for developing smart systems, smart homes, and smart cities. Given Industry 4.0 is comprised sensor fields, actuators, fog and cloud processing paradigms, and network systems, designing a secure architecture faces two major challenges: handling heterogeneous sources at scale and maintaining security over a large, disparate, data-driven system that interacts with the physical environment. This paper addresses these challenges by proposing a new threat intelligence scheme that models the dynamic interactions of industry 4.0 components including physical and network systems. The scheme consists of two components: a smart management module and a threat intelligence module. The smart data management module handles heterogeneous data sources, one of the foundational requirements for interacting with an Industry 4.0 system. This includes data to and from sensors, actuators, in addition to other forms of network traffic. The proposed threat intelligence technique is designed based on beta mixture-hidden Markov models (MHMMs) for discovering anomalous activities against both physical and network systems. The scheme is evaluated on two well-known datasets: the CPS dataset of sensors and actuators and the UNSW-NB15 dataset of network traffic. The results reveal that the proposed technique outperforms five peer mechanisms, suggesting its effectiveness as a viable deployment methodology in real-Industry 4.0 systems.