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.

This work describes an integrated solution to face a civil security problem in the area of Search And Rescue (SAR) of missing people. This proposal is based on the use of emerging technologies such as Unmanned Aerial Vehicles (UAV), also known as drones, and the use of simulated beacons on smartphones. In particular, in the presented tool, drones fly synchronously in a specific area so that each drone uses on-board sensors to scan and detect any signal emitted by Bluetooth Low Energy (BLE) beacons from smartphones of missing people. This technique allows getting the GPS position of any detected missing person. This work also includes some security issues related to possible attacks focused on the perimeter and physical security.

This paper describes the use of a new extension of the Bluetooth connection protocol, called FatBeacon, which faces the problem of obtaining information where no Internet connection is available. Rather than advertising a URL to load a web page, the FatBeacon protocol has the ability to broadcast any basic web contents actually hosted on the device. In particular, FatBeacons are here used to improve the tourist experience in places with no Internet coverage through a new application of the Internet of Things (IoT). Thanks to the fact that the web content is emitted by the own FatBeacon, any smartphone with Bluetooth Low Energy (BLE) can be used to receive touristic information, even in uncovered areas, such as rural or mountain destinations. This work does not only show the applicability of the new FatBeacon protocol, but it also presents a performance comparison of different BLE technologies used for similar touristic applications.

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