Summary With the arrival of the Internet of Things (IoT) many devices such as sensors, nowadays can communicate with each other and share data easily. However, the IoT paradigm is prone to security concerns as many attackers try to hit the network and make it vulnerable. In this scenario, security concerns are the most important and to address them various models have been designed to overcome these security issues, but still there exist many emerging variants of botnet attacks such as Mirai, Persirai, and Bashlite that exploits the security breaches. This research article aims to investigate cyber security in the advent of B‐IDS, DDOS, and malware attacks. For this purpose, different machine learning algorithms, namely, support vector machine, naive Bayes, linear regression, artificial neural network, decision tree, random forest, the fuzzy classifier, K‐nearest neighbor, adaptive boosting, gradient boosting, and tree ensemble have been implemented for botnet attack detection. For performance measures, these algorithms have been tested on nine sensor devices over N‐BaIoT datasets to measure the security and accuracy of the intrusion detection system. The results show that the tree‐based algorithm achieved more than 99% accuracy which is quite higher as compared to other tested methods on the same sensor devices.

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Multivariate statistical techniques involving factor analysis (FA) and R-mode hierarchical cluster analysis (HCA) were performed on 30 groundwater samples from Rangampeta, Chittoor District, Andhra Pradesh, South India to extract principal processes controlling the water chemistry. The groundwater samples were analyzed for distribution of chemical elements Ca, Mg, Na, K, Si, HCO 3 , CO 3 , Cl, and SO 4 . It also includes pH, and electrical conductivity (EC). Gibbs diagrams were also constructed to identify the processes that are responsible in controlling the water chemistry. Factor analysis extracted for four factors consisting of F1 (with high loading factor of Cl, EC, Mg and Na), F2 (with high loading factor of K, (HCO 3 +CO 3 ) and Ca), F3 (with high loading factor of pH and Si) and F4 (with high loading factor of SO 4 ). The varifactors obtained from Factor analysis indicated that the parameters responsible for groundwater quality variations are mainly related to groundwater-rock interaction (particularly weathering of silicate minerals), agriculture and anthropogenic sources. With HC analysis the water samples have been classified into 4 clusters. Cluster I (13 wells) and cluster II (8 wells) have shown moderate salinity. However, cluster IV (4 wells) had the lowest concentrations of ions and classified as fresh water. Cluster III (5 wells) shows mid salinity between (I and II) and IV clusters. The distribution of these groundwater types and their quality has been found to be an in direct relation with the host rocks of the area. The results showed that the method was comprehensive and efficient in analyzing the dynamics of water quality.

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