An exploration of utilising deep learning models in the realm of cyber-physical systems

Different attacks can trigger device failure, malfunction, etc. As such, the implementation of the cyber protection program for upcoming cyber physical systems (CPSs) can involve an enhanced security framework. The numerous cyber-detection systems focused on the deep learning algorithm was developed to identify and mitigate cyber-attacks of different types via CPSs, smart grids, power networks, etc. This paper provides a thorough analysis into various deep learning algorithms for cyber security implementations for CPSs. CPS fusion and agriculture may enhance food and environmental efficiency. Many researchers have therefore been carried out in this area to tackle problems, such as the shortage of information systems and networks, inadequate cooperation for a broader internet of thing solutions and complex shifts in internal or external technological conditions in precision agriculture. In this study, we concentrate on the creation of a method for improving prediction and tackle incorrect information due to the complex problem of precise farming. As an assessment, we first forecast a rainfall using weather sensor data and then the prediction effects are set as a supplement to prevent the effects of the surveillance system with water sprinkles.