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Anomaly detection has gained considerable attention in the past couple of years. Emerging technologies, such as the Internet of Things (IoT), are known to be among the most critical sources of data streams that produce massive amounts of data continuously from numerous applications. Examining these collected data to detect suspicious events can reduce functional threats and avoid unseen issues that cause downtime in the applications. Due to the dynamic nature of the data stream characteristics, many unresolved problems persist. In the existing literature, methods have been designed and developed to evaluate certain anomalous behaviors in IoT data stream sources. However, there is a lack of comprehensive studies that discuss all the aspects of IoT data processing. Thus, this paper attempts to fill this gap by providing a complete image of various state-of-the-art techniques on the major problems and core challenges in IoT data. The nature of data, anomaly types, learning mode, window model, datasets, and evaluation criteria are also presented. Research challenges related to data evolving, feature-evolving, windowing, ensemble approaches, nature of input data, data complexity and noise, parameters selection, data visualizations, heterogeneity of data, accuracy, and large-scale and high-dimensional data are investigated. Finally, the challenges that require substantial research efforts and future directions are summarized.

Most e-Learning web application known as Learning Management Systems are associated with collaboration in a web page. It allows a user to interact directly with multiple application in any web platform together with other users. However, the action of the users has not been thoroughly analyzed. Due to the medium of teaching, implementation is through online. It is nec-essary to analyse each student behaviour characteristics of blended learning implementation so that lecturer can adjust how online activities are per-formed. In this paper, we propose a conceptual model in profiling student behaviour in e-Learning based on metadata approach and Community of In-quiry Model. We adopt a metadata approach in collecting student experience in e-Learning and Community of Inquiry Model to mapping the online stu-dent experiences. This conceptual model provides the basis for evaluating student behaviour characteristics in online learning with the goal of im-proved student engagement and online activity design.

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