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Cloud computing is a vast area which uses the resources cost-effectively. The performance aspects and security are the main issues in cloud computing. Besides, the selection of optimal features and high false alarm rate to maintain the highest accuracy of the testing are also the foremost challenges focused. To solve these issues and to increase the accuracy, an effective cloud IDS using Grasshopper optimization Algorithm (GOA) and Deep belief network (DBN) is proposed in this paper. GOA is used to choose the ideal features from the set of features. Finally, DBN is developed for classification according to their selected feasible features. The introduced IDS is simulated on the Python platform and the performance of the suggested model of deep learning is assessed based on statistical measures named as Precision, detection accuracy, f-measure and Recall. The NSL_KDD, and UNSW_NB15 are the two datasets used for the simulation, and the results showed that the proposed scheme achieved maximum classification accuracy and detection rate.

The condition monitoring of rotating machines for critical applications plays an important role in reducing downtime. With Industry 4.0, the role of IoT in online condition monitoring of electrical machines has gained considerable significance. The main aim of the paper is the use of IoT for online monitoring of motor parameters like current, temperature, vibration, and humidity and observing its online trending using a web server. Data can be accessed in form of graphs and widgets by visiting the web page. The advantage of this project is the real-time monitoring of the motor from any remote area and in case of any abnormality operating personnel can take necessary steps for preventing complete breakdown. The proposed work can help industry people in online monitoring of motors and in the future work can be extended for fault prediction and classification.

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