Abstract

Abstract
The relevance of the tourism industry to the overall sustainability of rural territories grows along with the demand for rural tourism destinations. Likewise, as the digital transition of rural tour operators takes place, their marketing initiatives also evolve towards a digital nature, which is why it is crucial to comprehend how the overall calibre of these activities might affect the perception of rural places, while also motivating tourists' travel intentions and, as a result, promoting the general sustainability of the destination. Thus, in this paper, we propose a novel conceptual model based on Delone and McLean's representative model of Information Systems Success Model, on Tan and Wu's arguments on tourism destinations' image relationship with tourists' visit intentions, and also on Verma's tourism destination brand equity concept. To validate the proposed model, an online focus group was developed involving several specialists whose opinions and perspectives corroborated the potential adequacy of the proposed artefact and, consequently, assumed its contribution and value. From this validation process, it was possible to highlight that digital marketing initiatives' overall quality influences both rural destinations' image and tourists' intention to visit these territories, that a positive image will trigger tourists' visit behaviour, and that these behaviours represent a valuable asset to rural destinations' global sustainability.

Abstract
Water scarcity is becoming an issue of more significant concern with a major impact on global sustainability. For it, new measures and approaches are urgently needed. Digital technologies and tools can play an essential role in improving the effectiveness and efficiency of current water management approaches. Therefore, a solution is proposed and validated, given the limited presence of models or technological architectures in the literature to support intelligent water management systems for domestic use. It is based on a layered architecture, fully designed to meet the needs of households and to do so through the adoption of technologies such as the Internet of Things and cloud computing. By developing a prototype and using it as a use case for testing purposes, we have concluded the positive impact of using such a solution. Considering this is a first contribution to overcome the problem, some issues will be addressed in a future work, namely, data and device security and energy and traffic optimisation issues, among several others.

Abstract
The application of the Internet of Things concept in domains such as industrial control, building automation, human health, and environmental monitoring, introduces new privacy and security challenges. Consequently, traditional implementation of monitoring and security mechanisms cannot always be presently feasible and adequate due to the number of IoT devices, their heterogeneity and the typical limitations of their technical specifications. In this paper, we propose an IP flow-based Intrusion Detection System (IDS) framework to monitor and protect IoT networks from external and internal threats in real-time. The proposed framework collects IP flows from an IoT network and analyses them in order to monitor and detect attacks, intrusions, and other types of anomalies at different IoT architecture layers based on some flow features instead of using packet headers fields and their payload. The proposed framework was designed to consider both the IoT network architecture and other IoT contextual characteristics such as scalability, heterogeneity, interoperability, and the minimization of the use of IoT networks resources. The proposed IDS framework is network-based and relies on a hybrid architecture, as it involves both centralized analysis and distributed data collection components. In terms of detection method, the framework uses a specification-based approach drawn on normal traffic specifications. The experimental results show that this framework can achieve approximate to 100% success and 0% of false positives in detection of intrusions and anomalies. In terms of performance and scalability in the operation of the IDS components, we study and compare it with three different conventional IDS (Snort, Suricata, and Zeek) and the results demonstrate that the proposed solution can consume fewer computational resources (CPU, RAM, and persistent memory) when compared to those conventional IDS.