Internet of Things (IoT) paradigm became particularly popular in the last couple of years in such a way that the devices are present in almost every home across the globe. Using cheap components one can connect any device to the internet and enable information collecting from the environment, making everyday life a lot easier. Even though it does bring multiple advantages to the table, at the same time it brings certain challenges and vulnerabilities that need to be addressed. In this paper we focus on Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks and we provide a review of the current architecture of Internet of Things which is prone to these.

Air pollution is a complex mixture of toxic components that has the direct impact on human health, life quality, and the environment. In this study, meteorological variables and concentration of air pollutants are used to predict the common air quality index (CAQI) in Bjelave neighborhood, Sarajevo, BiH. CAQI prediction models were built using five popular machine learning techniques in the air pollution domain: Support Vector Regression, Random Forest, Extreme Gradient Boosting, Multiple Linear Regression and Multilayer Perceptron, using three-year period data (2016–2018). Prediction performance was measured using regression metrics: R-squared and RMSE. Ensemble technique, Random Forest method achieved the best performance results from the five evaluated machine learning methods: R 2 = 0.99 and RMSE = 2.30, using the dataset when missing values were removed, and R 2 = 0.99 and RMSE = 2.58 using the dataset when missing values were imputed using linear regression method.