Abstract
Nowadays, Virtual Reality (VR) and Augmented Reality (AR) systems are not exclusively associated with the gaming industry. Their potential is also useful for other business areas such as healthcare, automotive, and educational domains. Companies need to accompany technological advances and enhance their business processes and thus, the adoption of VR or AR technologies could be advantageous in reducing resource usage or improving the overall efficiency of processes. However, before implementing these technologies, companies must be aware of potential cyberattacks and security risks to which these systems are subject. This study presents a survey of attacks related to VR and AR scenarios and their risk assessment when considering healthcare, automation, education, and gaming industries. The main goal is to make companies aware of the possible cyberattacks that can affect the devices and their impact on their business domain. © 2023 IEEE.

Abstract
Cloud computing is a modern technology that has become popular today. A large number of requests has made it essential to propose a resources allocation framework for arriving requests. The network can be made more efficient and less costly this way. The cloud-edge paradigm has been considered a growing research area in the computing industry in recent years. The increase in the number of customers and requests for cloud data centers (CDCs) has created the need for robust servers and low power consumption mechanisms. Ways to reduce energy in the CDC having appropriate algorithms for resource allocation. The purpose of this study was to develop an intelligent method for dynamic resource allocation using Takagi-Sugeno-Kang (TSK) neural-fuzzy systems and ant colony optimization (ACO) techniques to reduce energy consumption by optimizing resource allocation in cloud networks. It predicts future loads using a drop-down window to track CPU usage. By optimizing virtual machine migration, ACO can reduce energy consumption. Simulations are provided by examining the implementation and a variety of parameters such as the number of requests made wasted resources, and requests rejected. In this paper, we propose the use of virtual machine migration to accomplish two main goals: evacuating additional and non-optimal virtual machines (scaling and shutting down additional active physical machines) and solving the resource granulation problem. We evaluated and compared our results with literature for rejection rates of virtual and physical machine applications. The performances of our algorithms are compared to different criteria such as performance in request rejection, dynamic CPU resource allocation with reinforcement learning, multi-objective resource allocation, NSGAIII, Whale optimization and Forecast Particle Swarm allocation. A comparison of some evaluation criteria showed that the proposed method is more efficient than other methods.

Abstract

Abstract
Vulnerability scanning tools are essential in detecting systems weaknesses caused by vulnerabilities in their components or wrong configurations. Corporations may use these tools to assess a system in advance and fix its vulnerabilities, thus preventing or mitigating the impact of real attacks. A set of these tools are organized by plugins, each intended to check a specific vulnerability, such as the case of the Tsunami Security Scanner tool released in 2020 by Google. Multiple plugins for this tool were proposed in a community-based approach and thus, it is important for the users and research community to have these plugins in a framework consistently categorized across multiple sources and types. This paper proposes a comprehensive taxonomy for all the 61 plugins available, hierarchically sorted into 2 main categories, 4 categories, 4 subcategories, and 7 types. An analysis and a discussion on statistics by categories and types over time are also provided. The analysis shows that, so far, there are 4 main contributors, being Google, Community, Facebook, and Govtech. The Google source is still the top contributor counting 39 out of 61 plugins and the highest number of plugins available are in the RCE subcategory. The plugins available are mainly focused on critical and high vulnerabilities.

Abstract
Cybersecurity skills are of utmost importance to prevent or mitigate the impact of cyberattacks. In higher education, there are graduations related to Information Technology (IT), where students are expected to develop technical skills, including cybersecurity. Thus, it is relevant to assess students' cybersecurity awareness regarding cybersecurity hygiene and cyber threats when they start their academic studies and to verify whether there are context-dependent differences. This paper presents the results of an assessment regarding the cybersecurity awareness level of 110 first-year students from computer science graduations from two different countries, Poland and Portugal. The assessment was designed as a survey divided into the following two main groups of questions: (1) awareness regarding cybersecurity hygiene and (2) awareness regarding major cyber threats considered in the European Union Agency for Cybersecurity (ENISA) 2021 cyber threat report. The survey results show that Polish and Portuguese students present different self-perceptions and knowledge regarding cybersecurity hygiene and knowledge of cybersecurity. In these areas, Polish students are generally more confident than Portuguese students. Also, Polish students presented better scores around 70%, against the ones obtained by the Portuguese students, scoring around 58%.

Abstract
Technology has enabled many devices to exchange huge amounts of data and communicate with each other as Edge Intelligence in Smart Cities (EISC), as a result of rapid technological advancements. When dealing with personal data, it is paramount to ensure that it is not disclosed and that there is no disclosure of any confidential information. In recent decades, academics and industry have spent considerable time and energy discussing security and privacy. Other systems, known as intrusion detection systems, are required to breach firewalls, antivirus software, and other security equipment to provide complete system security in smart operation systems. There are three aspects to an intrusion detection system: the intrusion detection method, the architecture, and the intrusion response method. In this study, we combined linear correlation feature selection methods and cross-information. The database used in this article is KDD99. This paper examines applying two feature selection methods in predicting attacks in intrusion detection systems based on INTERACT and A multilayer perceptron (MLP). Since the number of records associated with each attack type differs, one of our suggestions is to continue using data balancing techniques. As a result, the number of records associated with each type of network status becomes closer together. The results in the categories can also be improved using information synthesis methods, such as majority voting.