Smart Campus is an entity of any kind that uses technology and infrastructure to support and improve its processes, so people can use them better. This paper reviews the literature to contextualize the Internet of Things and its vital importance for the Smart Campus, as well as its relationship with the concepts of cybersecurity and wireless sensor network. We describe the various interrelationships, tendencies, and future development of a Smart Campus, as well as the differences and similarities with the emerging concept of Smart University. This review revealed that the Internet of Things is involved in all fields and can influence and improve the university’s processes to contribute to decision-making, technological development, and academic learning. To conclude, Smart University focuses on improving the infrastructure of universities through technology, with the main purpose of enhancing the quality of the education provided by institutions.

Smart University is an emerging concept, strongly anchored to smart technologies and considered by different authors in the literature. Organizations, including universities, need to incorporate smart technologies to take advantage of their capabilities to transform their processes and drive them toward new organizational models. A Smart University focuses on improving its technological infrastructure for achieving its quality educational goals. This paper presents the integration of the key factors for adopting four smart technologies: Cloud Computing, Big Data, Artificial Intelligence, and the Internet of Things. This characterization and integration allow us to conclude on the need to align digital technologies with the organization's processes, requiring greater interaction with the company’s senior management.

The importance of network traffic classification has grown over the last two decades in line with the increasing diver- sity of networked applications. Nowadays traditional approaches to traffic classification, relying on port numbers and on Deep Packet Inspection (DPI), are not very effective in real scenarios respectively due to the usage of random or non-standard port numbers and to the wide usage of end-to-end encryption. Despite their limitations, port- based and DPI approaches are still widely used in operational networks for a number of network monitoring and management tasks. This paper proposes a practical approach for improving the efficiency of traditional traffic classification techniques by chain- ing fast classification stages (port-based and machine-learning- based), combined to lower their false-positive rate, and a more precise - but time- and resource-demanding - stage based on DPI. Experimental results demonstrate that Chain obtains results in line with DPI approaches in term of Precision, Recall, Accuracy and Area Under the Curve (AUC), while it is 45% faster when compared to nDPIng, a well- known DPI implementation. The appealing of the proposed approach in Network Function Virtualization (NFV) contexts is also discussed.