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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with the emergence of internet and World Wide Web, traditional businesses got a new opportunity to compete globally. Traditional bricks-and-mortar businesses became electronic business (e-business) by utilizing Information and Communication Technology (ICT) tools. A new term of Mobile Commerce (M-Commerce) has created tremendous spectrum of business opportunities for businesses. Although there has been large scale adoption of M-Commerce in developingeconomies, but little growth is observed in developing economies such as Pakistan. There are doubts that users of Mcommerce demonstrate a lack of enthusiasm, which may be due to lack of trustworthiness. Based on the well-known and widely used Technology Acceptance Model (TAM), this research study provides the conceptual framework, underpinning the relationship of trust with the adoption of Mcommerce in Pakistan. The aim of this paper is to study the role of trust and its relationship with the acceptance of MCommerce in Pakistan.

In computer science field, one of the basic operation is sorting. Many sorting operations use intermediate steps. Sorting is the procedure of ordering list of elements in ascending or descending with the help of key value in specific order. Many sorting algorithms have been designed and are being used. This paper presents performance comparisons among the two sorting algorithms, one of them merge sort another one is quick sort and produces evaluation based on the performances relating to time and space complexity. Both algorithms are vital and are being focused for long period but the query is still, which of them to use and when. Therefore this research study carried out. Each algorithm resolves the problem of sorting of data with a unique method. This study offers a complete learning that how both of the algorithms perform operation and then distinguish them based on various constraints to come with outcome.

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