Internet of Things (IoT) is promising technology that brings tremendous benefits if used optimally. At the same time, it has resulted in an increase in cybersecurity risks due to the lack of security for IoT devices. IoT botnets, for instance, have become a critical threat; however, systematic and comprehensive studies analyzing the importance of botnet detection methods are limited in the IoT environment. Thus, this study aimed to identify, assess and provide a thoroughly review of experimental works on the research relevant to the detection of IoT botnets. To accomplish this goal, a systematic literature review (SLR), an effective method, was applied for gathering and critically reviewing research papers. This work employed three research questions on the detection methods used to detect IoT botnets, the botnet phases and the different malicious activity scenarios. The authors analyzed the nominated research and the key methods related to them. The detection methods have been classified based on the techniques used, and the authors investigated the botnet phases during which detection is accomplished. This research procedure was used to create a source of foundational knowledge of IoT botnet detection methods. As a result of this study, the authors analyzed the current research gaps and suggest future research directions.

In the last years, online social networks (ONSs, online social networks) have grown to become one of the major medias for human beings on the Internet by having a wide distribution and acceptance by users. One of the critical aspects of developing this type of systems is the user interface. For this reason, a modelbased approach is used to allow us collect interaction and collaboration aspects of social networks. Moreover, this paper based on ONSs and model-driven development provides a first approximation of social and collaborative model of a new online social network, which we have called PRSN (Positive Reinforcement Online Social Network).