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IoT is characterized by communication between things (devices) that constantly share data, analyze, and make decisions while connected to the internet. This interconnected architecture is attracting cyber criminals to expose the IoT system to failure. Therefore, it becomes imperative to develop a system that can accurately and automatically detect anomalies and attacks occurring in IoT networks. Therefore, in this paper, an Intrsuion Detection System (IDS) based on extracted novel feature set synthesizing BoT-IoT dataset is developed that can swiftly, accurately and automatically differentiate benign and malicious traffic. Instead of using available feature reduction techniques like PCA that can change the core meaning of variables, a unique feature set consisting of only seven lightweight features is developed that is also IoT specific and attack traffic independent. Also, the results shown in the study demonstrates the effectiveness of fabricated seven features in detecting four wide variety of attacks namely DDoS, DoS, Reconnaissance, and Information Theft. Furthermore, this study also proves the applicability and efficiency of supervised machine learning algorithms (KNN, LR, SVM, MLP, DT, RF) in IoT security. The performance of the proposed system is validated using performance Metrics like accuracy, precision, recall, F-Score and ROC. Though the accuracy of Decision Tree (99.9%) and Randon Forest (99.9%) Classifiers are same but other metrics like training and testing time shows Random Forest comparatively better.

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Weather forecasting has been an area of considerable interest among researchers since long. A scientific approach to weather forecasting is highly dependent upon how well the atmosphere and its interactions with the various aspects of the earth surface is understood. Applicability of artificial neural networks (ANNs) in forecasting has led to tremendous surge in dealing with uncertainties. This paper focuses on analysis and selection of various techniques used in developing a suitable feed forward neural network for forecasting 24hr ahead hourly temperature using MATLAB 7.6.0 neural network toolbox. The data of 60 days hourly data temperature is used to train and test the different models, training functions, activation functions, learning functions, performance functions and the most suitable combination is selected. The performance and reliability of these models are then evaluated by number of statistical measures. Results are compared with each training function.