Abstract
In the past years, the automotive industry has been integrating multiple hardware in the vehicle to enable new features and applications. In particular automotive applications, it is important to monitor the actions and behaviors of drivers and passengers to promote their safety and track abnormal situations such as social disorders or crimes. These applications rely on multiple sensors that generate real-time data to be processed, and thus, they require adequate data acquisition and analysis systems.This article proposes a prototype to enable in-vehicle data acquisition and analysis based on the middleware framework Robot Operating System (ROS). The proposed prototype features two processing devices and enables synchronized audio and video acquisition, storage, and processing. It was assessed through the implementation of a live inference system consisting of a face detection algorithm from the data gathered from the cameras and the microphone. The proposed prototype inherits the flexibility of the ROS framework and has a modular and scalable design; thus, more sensors, processing devices, and applications can be deployed.

Abstract
One of the technologies based on information technology used today is the VANET network used for inter-road communication. Today, many developed countries use this technology to optimize travel times, queue lengths, number of vehicle stops, and overall traffic network efficiency. In this research, we investigate the critical and necessary factors to increase the quality of VANET networks. This paper focuses on increasing the quality of service using multi-agent learning methods. The innovation of this study is using artificial intelligence to improve the network's quality of service, which uses a mechanism and algorithm to find the optimal behavior of agents in the VANET. The result indicates that the proposed method is more optimal in the evaluation criteria of packet delivery ratio (PDR), transaction success rate, phase duration, and queue length than the previous ones. According to the evaluation criteria, TSR 6.342%, PDR 9.105%, QL 7.143%, and PD 6.783% are more efficient than previous works.

Abstract
An intrusion detection system (IDS) is an important tool to prevent potential threats to systems and data. Anomaly-based IDSs may deploy machine learning algorithms to classify events either as normal or anomalous and trigger the adequate response. When using supervised learning, these algorithms require classified, rich, and recent datasets. Thus, to foster the performance of these machine learning models, datasets can be generated from different sources in a collaborative approach, and trained with multiple algorithms. This paper proposes a vote-based architecture to generate classified datasets and improve the performance of supervised learning-based IDSs. On a regular basis, multiple IDSs in different locations send their logs to a central system that combines and classifies them using different machine learning models and a majority vote system. Then, it generates a new and classified dataset, which is trained to obtain the best updated model to be integrated into the IDS of the companies involved. The proposed architecture trains multiple times with several algorithms. To shorten the overall runtimes, the proposed architecture was deployed in Fed4FIRE+ with Ray to distribute the tasks by the available resources. A set of machine learning algorithms and the proposed architecture were assessed. When compared with a baseline scenario, the proposed architecture enabled to increase the accuracy by 11.5% and the precision by 11.2%.

Abstract
Currently, there are several security-related standards and recommendations concerning Domain Name System (DNS) and Hypertext Transfer Protocol (HTTP) services, that are highly valuable for governments and their services, and other public or private organizations. This is also the case of Higher Education Institutions (HEIs). However, since these institutions have administrative autonomy, they present different statuses and paces in the adoption of these web-related security services. This paper presents an overview regarding the implementation of security standards and recommendations by the Portuguese HEIs. In order to collect these results, a set of scripts were developed and executed. Data were collected concerning the security of the DNS and HTTP protocols, namely, the support of Domain Name System Security Extensions (DNSSEC), HTTP main configurations and redirection, digital certificates, key size, algorithms and Secure Socket Layer (SSL)/Transport Layer Security (TLS) versions used. The results obtained allow to conclude that there are different progresses between HEIs. In particular, only 11.7% of HEIs support DNSSEC, 14.4% do not use any SSL certificates, 74.8% use a 2048 bits encryption key, and 81.1% use the Rivest-Shamir-Adleman (RSA) algorithm. Also, 6.3% of HEIs still negotiate with the vulnerable SSLv3 version. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
This research aims to represent a novel approach to detect malicious nodes in Ad-hoc On-demand Distance Vector (AODV) within the next-generation smart cities. Smart city applications have a critical role in improving public services quality, and security is their main weakness. Hence, a systematic multidimensional approach is required for data storage and security. Routing attacks, especially sinkholes, can direct the network data to an attacker and can also disrupt the network equipment. Communications need to be with integrity, confidentiality, and authentication. So, the smart city and urban Internet of Things (IoT) network, must be secure, and the data exchanged across the network must be encrypted. To solve these challenges, a new protocol using CLustering Multi-Layer Security Protocol (CL-MLSP) with AODV has been proposed. The Advanced Encryption Standard (AES) algorithm is aligned with the proposed protocol for encryption and decryption. The shortest path is obtained by the clustering method based on energy, mobility, and distribution for each node. Ns2 is used to evaluate the CL-MLSP performance, and the parameters are network lifetime, latency, packet loss, and security. We have compared CL-MLPS with ECP-AODV, Probe, and Multi-Path. The proposed method superiority rates in energy consumption, drop rate, delay, throughput, and security performance are 6.54%, 12.87%, 8.12%, 9.46%, respectively.

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.