This article presents a practical intrusion detection pipeline for IoT and corporate network environments. The approach combines several deep neural network models trained on different traffic domains with a RandomForest meta-learner and an XGBoost multiclass stage. The workflow covers data preprocessing, model training, SHAP-based threshold tuning and deployment for real-time inference on both GPU servers and embedded platforms. The reported evaluation highlights high detection performance, low inference latency and a substantial reduction of benign traffic before multilayer classification.

Safeguarding digital information against unauthorized access is critical in the industry context. Techniques for cracking passwords are essential tools for both attackers and defenders. This study explores the utilization of Bloom filters, a probabilistic data structure known for its space and time efficiency, to refine the password-cracking process. We demonstrate that by employing Bloom filters, it is possible to significantly enhance the performance of password-cracking techniques in terms of speed and memory consumption. By conducting a comparative analysis with prevalent techniques such as hash tables and binary search, we demonstrate the superior performance of Bloom filters. The experimentation, utilizing a publicly available dataset of leaked password hashes, indicates a significant improvement in cracking efficiency. The findings contribute to the broader cybersecurity goal of developing resilient systems against password-related breaches, underscoring the importance of integrating cutting-edge research and practical applications to fortify digital defenses.

Abstract Cyberthreats continue their expansion, becoming more and more complex and varied. However, credentials and passwords are still a critical point in security. Password cracking can be a powerful tool to fight against cyber criminals if used by cybersecurity professionals and red teams, for instance, to evaluate compliance with security policies or in forensic investigations. For particular systems, one crucial step in the password-cracking process is comparison or matchmaking between password-guess hashes and real hashes. We hypothesize that using newer data structures such as Cuckoo filters could optimize this process. Experimental results show that, with a proper configuration, this data structure is two orders of magnitude more efficient in terms of size/usage compared to other data structures while keeping a comparable performance in terms of time.

The growth in mobile network data traffic is even more noticeable in networks compatible with the Long‐Term Evolution (LTE) standard. As such, both academia and industry seek mechanisms aimed at improving new communications systems, therefore, new test benches are required. The emulators are candidates to validate the proposals without affecting the real central systems. However, many papers that seek to address LTE emulation ignore the not‐so‐insignificant effect of wireless media propagation features. In this work, a characterization of the main propagation models used by typical emulation systems has been carried out. The network simulator three tool using indoor propagation models, has compared the outcomes achieved compared with the results obtained in a real test bench. Since most mobile data in the coming years is forecast to be mobile video traffic, this study focuses on a live video streaming service, considering both real‐time message protocol and real‐time streaming protocol transmission protocols. From the results, we demonstrate that only one of the propagation models under study is able to equate with a real LTE system performance. Thereby, the emulation system under this specific setup is well suited to conducting performance tests of LTE networks supporting video streaming services in indoor scenarios.

The disclosure of personal and private information is one of the main challenges of the Internet of Medical Things (IoMT). Most IoMT-based services, applications, and platforms follow a common architecture where wearables or other medical devices capture data that are forwarded to the cloud. In this scenario, edge computing brings new opportunities to enhance the operation of IoMT. However, despite the benefits, the inherent characteristics of edge computing require countermeasures to address the security and privacy issues that IoMT gives rise to. The restrictions of IoT devices in terms of battery, memory, hardware resources, or computing capabilities have led to a common agreement for the use of elliptic curve cryptography (ECC) with hardware or software implementations. As an example, the elliptic curve digital signature algorithm (ECDSA) is widely used by IoT devices to compute digital signatures. On the other hand, it is well known that dual signature has been an effective method to provide consumer privacy in classic e-commerce services. This article joins both approaches. It presents a novel solution to enhanced security and the preservation of data privacy in communications between IoMT devices and the cloud via edge computing devices. While data source anonymity is achieved from the cloud perspective, integrity and origin authentication of the collected data is also provided. In addition, computational requirements and complexity are kept to a minimum.

Nowadays, the popularity of the unmanned aerial vehicles (UAVs) is high, and it is expected that, in the next years, the implementation of UAVs in day-to-day service will be even greater. These new implementations make use of novel technologies encompassed under the term Internet of Things (IoT). One example of these technologies is Long-Range (LoRa), classified as a Low-Power Wide-Area Network (LPWAN) with low-cost, low-power consumption, large coverage area, and the possibility of a high number of connected devices. One fundamental part of a proper UAV-based IoT service deployment is performance evaluation. However, there is no standardized methodology for assessing the performance in these scenarios. This article presents a case study of an integrated UAV-LoRa system employed for air-quality monitoring. Each UAV is equipped with a set of sensors to measure several indicators of air pollution. In addition, each UAV also incorporates an embedded LoRa node for communication purposes. Given that mobility is key when evaluating the performance of these types of systems, we study eight different mobility models, focusing on the effect that the number of UAVs and their flying speed have on system performance. Through extensive simulations, performance is evaluated via multiple quality dimensions, encompassing the whole process from data acquisition to user experience. Results show that our performance evaluation methodology allows a complete understanding of the operation, and for this specific case study, the mobility model with the best performance is Pathway because the LoRa nodes are distributed and move orderly throughout the coverage area.

Estimating the number of people in a given environment is an attractive tool with a wide range of applications. Urban environments are not an exception. Counting pedestrians and locate them properly in a road traffic scenario can facilitate the design of intelligent systems for traffic control that take into account more actors and not only vehicle-based optimizations. In this work, we present a new WiFi-based passive method to estimate the number of pedestrians in an urban traffic scenario formed by signaled intersections. Particularly, we are able i) to distinguish between pedestrians walking and pedestrians waiting in a pedestrian crossing and ii) to estimate the exact location where static pedestrians are waiting to cross. By doing so, the pedestrian factor could be included in intelligent control management systems for traffic optimization in real time. The performance analysis carried out shows that our method is able to achieve a significant level of accuracy while presenting an easy implementation.

Currently, 4G mobile communication systems are supported by the 3GPP standard. In view of the significant increase in mobile data traffic, it is necessary to characterize it to improve the performance of current wireless networks. Indeed, video transmission and video streaming are fundamental assets for the upcoming smart cities and urban environments. Due to the high costs of deploying a real LTE system, emulation systems that consider real operating conditions emerge as a successful alternative. On the other hand, many studies with LTE simulations and emulations do not present information of basic adjustment parameters like the propagation model, nor of validation of the results with real conditions. This paper shows the validation with an ANOVA statistical analysis of an LTE emulation system developed in NS-3 for the live video streaming service. For the validation, different QoS parameters and real conditions have been used. Also, two protocols, namely RTMP and RTSP, have been tested. It is demonstrated that the emulation scenario is appropriate to characterize the traffic that will later allow to carry out a proper performance analysis of the service and technology under study.

The increase in multimedia services has put energy saving on the top of current demands for mobile devices. Unfortunately, batteries' lifetime has not been as extended as it would be desirable. For that reason, reducing energy consumption in every task performed by these devices is crucial. In this work, a novel opportunistic routing protocol, called JOKER, is introduced. This proposal presents novelties in both the candidate selection and coordination phases, which permit increasing the performance of the network supporting multimedia traffic as well as enhancing the nodes' energy efficiency. JOKER is compared in different-nature test-benches with BATMAN routing protocol, showing its superiority in supporting a demanding service such as video-streaming in terms of QoE, while achieving a power draining reduction in routing tasks.

MANETs are experiencing unstoppable growth due to their decentralized topology and their easy and low-cost deployment. The dynamic nature of MANETs entails the use of appropriate routing protocols to obtain good performance in the services supported by the network; even more if these services present tough temporal requirements, such as multimedia or VoIP services. One routing protocol to which notable research effort has been devoted is the BATMAN protocol, which proposes an interesting paradigm avoiding the explicit interchange of routing information among nodes. In this paper, we focus on evaluating the performance of BATMAN supporting VoIP traffic on low power-consumption nodes, from a Quality of Experience (QoE) point of view. Specifically, we evaluate the impact on BATMAN performance of 1) the PHY layer, by employing a fading characterization of the transmission channel; 2) the number and density of ad-hoc nodes; and 3) node mobility. All the results obtained for BATMAN are compared with those attained by using the widely used OLSR routing protocol. From the results, we conclude that neither BATMAN nor OLSR in their respective current implementations are suitable enough for VoIP traffic support in MANETs composed of energy-saving nodes.

This paper studies the effect of including continuous assessments in the methodology of an introductory Telematics course for Telecommunication Engineers. Seven academic years were evaluated, corresponding to a total number of 364 students. Whereas the methodology of the first three years was based only on lectures and lab activities, the last four years included assessments (e.g., problem resolution, information searching, reports, tests, etc.) as an integral part of the students' final scores. Through statistical analysis, results reveal that there is no significant difference in the pass rate between years with assessments and years without assessments, taking into account all students enrolled in the course. However, there is a significant (statistical) difference in the fail and dropout rates. On the other hand, if only students who did not dropout the course are taken into account, then there is a significant (statistical) difference in the pass rate comparing both methodologies: including the assessments increased the pass rate from 53% to 72%. Nevertheless, no differences were found in the average final grades obtained by the students with either methodology. Regarding the students' gender, the effect of the assessments was higher for male students than for female students.

Recent demands for new applications are giving rise to an increasing need of quality of service (QoS). Nowadays, most IP-based networks tend to use the DiffServ architecture to provide end-to-end QoS. Traffic conditioners are a key element in the deployment of DiffServ. In this paper, we introduce a new approach for traffic conditioning based on feedback signaling among boundary nodes and traffic conditioners. This new approach is intended to provide a proportional distribution of excess bandwidth to end-users. We evaluate through extensive simulations the performance of our proposal in terms of final throughput, considering contracted target rates and distribution of spare bandwidth. Results show a high level of fairness in the excess bandwidth allocation among TCP sources under different network conditions.

The counter-based modified (CBM) traffic conditioner was presented in a previous work as a feasible election for the assured service implementation with Diffserv. In this work, we evaluate the end-to-end performance of TCP Reno sources with the CBM traffic conditioner in topologies with one and three networks nodes under miscellaneous characteristics, i.e. variable contracted target rates and roundtrip times. In addition and for the same scenarios, the robustness of the couple CBM-RIO is tested when assured service and best-effort sources coexist and compete target bandwidths and even allocation of spare bandwidth across different flows can be provided as shown in simulation results.