This paper presents a study of positioning system that provides advanced information services based on Wi-Fi and Bluetooth Low Energy (BLE) technologies. It uses Wi-Fi for rough positioning and BLE for fine positioning. It is designed for use in public transportation system stations and terminals where the conditions are “hostile” or unfavourable due to signal noise produced by the continuous movement of passengers and buses, data collection conducted in the constant presence thereof, multipath fading, non-line of sight (NLOS) conditions, the fact that part of the wireless communication infrastructure has already been deployed and positioned in a way that may not be optimal for positioning purposes, variable humidity conditions, etc. The ultimate goal is to provide a service that may be used to assist people with special needs. We present experimental results based on scene analysis; the main distance metric used was the Euclidean distance but the Mahalanobis distance was also used in one case. The algorithm employed to compare fingerprints was the weighted k-nearest neighbor one. For Wi-Fi, with only three visible access points, accuracy ranged from 3.94 to 4.82 m, and precision from 5.21 to 7.0 m 90% of the time. With respect to BLE, with a low beacon density (1 beacon per 45.7 m2), accuracy ranged from 1.47 to 2.15 m, and precision from 1.81 to 3.58 m 90% of the time. Taking into account the fact that this system is designed to work in real situations in a scenario with high environmental fluctuations, and comparing the results with others obtained in laboratory scenarios, our results are promising and demonstrate that the system would be able to position users with these reasonable values of accuracy and precision.

Data mining and information extraction from data is a field that has gained relevance in recent years thanks to techniques based on artificial intelligence and use of machine and deep learning. The main aim of the present work is the development of a tool based on a previous behaviour study of security audit tools (oriented to SQL pentesting) with the purpose of creating testing sets capable of performing an accurate detection of a SQL attack. The study is based on the information collected through the generated web server logs in a pentesting laboratory environment. Then, making use of the common extracted patterns from the logs, each attack vector has been classified in risk levels (dangerous attack, normal attack, nonattack, etc.). Finally, a training with the generated data was performed in order to obtain a classifier system that has a variable performance between 97 and 99 percent in positive attack detection. The training data is shared to other servers in order to create a distributed network capable of deciding if a query is an attack or is a real petition and inform to connected clients in order to block the petitions from the attacker's IP.

This work presents a system to detect small boats (pateras) to help tackle the problem of this type of perilous immigration. The proposal makes extensive use of emerging technologies like Unmanned Aerial Vehicles (UAV) combined with a top-performing algorithm from the field of artificial intelligence known as Deep Learning through Convolutional Neural Networks. The use of this algorithm improves current detection systems based on image processing through the application of filters thanks to the fact that the network learns to distinguish the aforementioned objects through patterns without depending on where they are located. The main result of the proposal has been a classifier that works in real time, allowing the detection of pateras and people (who may need to be rescued), kilometres away from the coast. This could be very useful for Search and Rescue teams in order to plan a rescue before an emergency occurs. Given the high sensitivity of the managed information, the proposed system includes cryptographic protocols to protect the security of communications.

Vehicular ad hoc networks (VANETs) is considered a milestone in improving the safety and efficiency in transportation. Nevertheless, when information from the vehicular communications is combined with data from the cloud, it also introduces some privacy risks by making it easier to track the physical location of vehicles. For this reason, to guarantee the proper performance of a VANET it is essential to protect the service against malicious users aiming at disrupting the proper operation of the network. Current researches usually define a traditional identity-based authentication for nodes, which are loaded with individual credentials. However, the use of these credentials in VANETs without any security mechanism enables vehicle tracking and therefore, violate users' privacy, a risk that may be overcome by means of appropriate anonymity schemes. This comes at the cost, however, of on the one hand preventing VANET centralized authorities from identifying malicious users and revoking them from the network, or on the other hand to avoid complete anonymity of nodes in front of the CA thus to allow their revocation. In this paper, a novel revocation scheme that is able to track and revoke specific malicious users only after a number of complaints have been received while otherwise guaranteeing node's k-anonymity is described. The proper performance of these mechanisms has been widely evaluated with NS-2 simulator and an analytical model validated with scripts. The results show that presented work is a promising approach in order to increase privacy protection while allowing revocation with little extra costs.

A vehicular ad hoc network (VANET) is a wireless network that provides communications between nearby vehicles. Among the different types of information that can be made available to vehicles through VANETs, road traffic information is the most important one. This work is part of an experimental development of a wireless communication platform oriented to applications that allow improving road efficiency and safety, managing and monitoring road traffic, encouraging cooperative driving, and offering pedestrian services and other added-value uses. The proposed system consists of smartphones, sensors, and Wi-Fi hotspots 2.0, as well as complementary functionalities including access to network infrastructure via 3G, GPRS, and 4G. The developed wireless network prototype allows taking advantage of the potential benefits of VANETs. At the same time, the use of smartphones does not require large money investments either in public or restricted areas. The first implementations with smartphones have been useful to test the behaviour of the proposal in a real environment. We have also implemented a large-scale simulation by using NS-2 simulator. From the obtained data, we have estimated the minimum requirements necessary for the correct working of a VANET and the problems that can happen in case of possible attacks or communication overhead.