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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

40 pages, 620 KB

Open AccessEditor’s ChoiceReview

A Systematic Review of Cybersecurity Risks in Higher Education

by Joachim Bjørge Ulven and Gaute Wangen

Future Internet 2021, 13(2), 39; https://doi.org/10.3390/fi13020039 - 2 Feb 2021

Cited by 124 | Viewed by 52118

Abstract

The demands for information security in higher education will continue to increase. Serious data breaches have occurred already and are likely to happen again without proper risk management. This paper applies the Comprehensive Literature Review (CLR) Model to synthesize research within cybersecurity risk by reviewing existing literature of known assets, threat events, threat actors, and vulnerabilities in higher education. The review included published studies from the last twelve years and aims to expand our understanding of cybersecurity’s critical risk areas. The primary finding was that empirical research on cybersecurity risks in higher education is scarce, and there are large gaps in the literature. Despite this issue, our analysis found a high level of agreement regarding cybersecurity issues among the reviewed sources. This paper synthesizes an overview of mission-critical assets, everyday threat events, proposes a generic threat model, and summarizes common cybersecurity vulnerabilities. This report concludes nine strategic cyber risks with descriptions of frequencies from the compiled dataset and consequence descriptions. The results will serve as input for security practitioners in higher education, and the research contains multiple paths for future work. It will serve as a starting point for security researchers in the sector. Full article

(This article belongs to the Special Issue Feature Papers for Future Internet—Cybersecurity Section)

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20 pages, 1172 KB

Open AccessEditor’s ChoiceArticle

Using Machine Learning for Web Page Classification in Search Engine Optimization

by Goran Matošević, Jasminka Dobša and Dunja Mladenić

Future Internet 2021, 13(1), 9; https://doi.org/10.3390/fi13010009 - 2 Jan 2021

Cited by 50 | Viewed by 17402

Abstract

This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research. Full article

(This article belongs to the Special Issue Digital Marketing and App-based Marketing)

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