Abstract
Cyberattacks are performed against all organizations including Higher Education Institutions (HEIs). When these attacks are successful, they can affect the regular operation of these institutions and may cause the leak of essential or sensitive data that can be misused or become inaccessible. Therefore, the adoption of current security services is important for devices and services exposed to the Internet that should run the latest and secure versions of web-related protocols and comply with the latest security-related guidelines and recommendations. This article surveys and analyzes the status of web-related security services, namely the Hyper Text Transfer Protocol Secure (HTTPS) and the Domain Name System Security Extensions (DNSSEC) services, in Brazilian HEIs. The results of this survey show that regarding HTTPS around 15% do not use any SSL / TLS certificate and of those supporting it, about 14% do not demand its usage. Regarding DNSSEC, the analysis shows that only around 2% of the HEIs are implementing this protocol. These results show that it is important to design an effective and continuous action plan for HEIs regarding the support or discontinuity of versions of these protocols, in order to improve their protection against cyberattacks.

Abstract
Low Power Wide Area Networks (LPWAN) are used worldwide in several Internet of Things (IoT) applications that rely on large-scale deployments. Despite most of these networks include their own security mechanisms with built-in encryption, they are still vulnerable to a range of attacks that can be performed using low-cost Software Defined Radio (SDR) hardware and low-complexity techniques. This work provides an experimental setup implemented to exploit physical layer vulnerabilities with SDR techniques. Several attack vectors typically related to LPWAN within the IoT ecosystem are implemented and tested such as Global Positioning (GPS) Spoofing, Replay Attacks, Denial-of-Service (DoS) and Jamming, in environments that rely specifically on LoRaWAN networks. The results show that, in LoRAWAN networks, a set of vulnerabilities can be exploited leading to the incorrect functioning of the executed applications, and possible damage to the systems in which they operate. It was possible to verify that, depending on the type of activation method used between the devices and the LoRaWAN server, the communications and the devices can be compromised.

Abstract
Answering a query through a peer-to-peer database presents one of the greatest challenges due to the high cost and time required to obtain a comprehensive response. Consequently, these systems were primarily designed to handle approximation queries. In our research, the primary objective was to develop an intelligent system capable of responding to approximate set-value inquiries. This paper explores the use of particle optimization to enhance the system's intelligence. In contrast to previous studies, our proposed method avoids the use of sampling. Despite the utilization of the best sampling methods, there remains a possibility of error, making it difficult to guarantee accuracy. Nonetheless, achieving a certain degree of accuracy is crucial in handling approximate queries. Various factors influence the accuracy of sampling procedures. The results of our studies indicate that the suggested method has demonstrated improvements in terms of the number of queries issued, the number of peers examined, and its execution time, which is significantly faster than the flood approach. Answering queries poses one of the most arduous challenges in peer-to-peer databases, as obtaining a complete answer is both costly and time-consuming. Consequently, approximation queries have been adopted as a solution in these systems. Our research evaluated several methods, including flood algorithms, parallel diffusion algorithms, and ISM algorithms. When it comes to query transmission, the proposed method exhibits superior cost-effectiveness and execution times.

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
Social engineering attacks such as phishing are performed against companies and institutions and thus, cybersecurity awareness and training of technical and non-technical human resources play a fundamental role in preventing and mitigating a set of cyberattacks. This paper presents a comparative study based on simulated phishing attacks on two organizations with contrasting security practices and procedures. The first organization is a secondary school, with no IT staff, no defined information security policy, no guidance from top management on cybersecurity issues, and no training actions. The other is a company with a permanent IT staff, a defined security policy, and where its employees receive regular cybersecurity awareness training exercises. Two simulated phishing attack scenarios were deployed to compare these organisations regarding the behaviour of their employees and the readiness of their IT staff and to verify if the employees’ academic degree is a decisive criterion to protect them against this type of attack. The main results show that the rapid reporting and action of the IT staff in the organization where it existed, was an effective measure to mitigate the impact of the simulated phishing attack. In addition, the results show that about 18% of school employees leaked their data, compared to about 10% of the company. Furthermore, this study allows us to deduce that the academic level of employees does not seem to be a decisive criterion to protect them against phishing attacks. © 2022, Innovative Information Science and Technology Research Group. All rights reserved.