The world has been afflicted by the rise of misinformation. The sheer volume of news produced daily necessitates the development of automated methods for separating fact from fiction. To tackle this issue, the computer science community has produced a plethora of approaches, documented in a number of surveys. However, these surveys primarily rely on one-dimensional solutions, i.e., deception detection approaches that focus on a specific aspect of misinformation, such as a particular topic, language, or source. Misinformation is considered a major obstacle for situational awareness, including cyber, both from a company and a societal point of view. This paper explores the evolving field of misinformation detection and analytics on information published in news articles, with an emphasis on methodologies that handle multiple dimensions of the fake news detection conundrum. We analyze and compare existing research on cross-dimensional methodologies. Our evaluation process is based on a set of criteria, including a predefined set of performance metrics, data pre-processing features, and domains of implementation. Furthermore, we assess the adaptability of each methodology in detecting misinformation in real-world news and thoroughly analyze our findings. Specifically, survey insights demonstrate that when a detection approach focuses on several dimensions (e.g., languages and topics, languages and sources, etc.), its performance improves, and it becomes more flexible in detecting false information across different contexts. Finally, we propose a set of research directions that could aid in furthering the development of more advanced and accurate models in this field.

The COVID-19 outbreak has forced businesses to shift to an unprecedented “work from home” company environment. While this provides advantages for employees and businesses, it also leads to a multitude of shortcomings, most prevalent of which is the emergence of additional security risks. Previous to the outbreak, company computer networks were mainly confined within its facilities. The pandemic has now caused this network to “spread thin,” as the majority of employees work remotely. This has opened up a variety of new vulnerabilities, as workers’ cyber protection is not the same at home as it is in office. Although the effects of the virus are now subsiding, working remotely has embedded itself as the new normal. Thus, it is imperative for company management to take the necessary steps to ensure business continuity and be prepared to deal with an increased number of cyber threats. In our research, we provide a detailed classification for a group of tools which will facilitate risk mitigation and prevention. We also provide a selection of automated tools such as vulnerability scanners, monitoring and logging tools, and antivirus software. We outline each tool using tables, to show useful information such as advantages, disadvantages, scalability, cost, and other characteristics. Additionally, we implement decision trees for each category of tools, in an attempt to assist in navigating the large amount of information presented in this paper. Our objective is to provide a multifaceted taxonomy and analysis of mitigation tools, which will support companies in their endeavor to protect their computer networks. Our contribution can also help companies to have some type of cyber threat intelligence so as to put themselves one step ahead of cyber criminals.