Fast GPU audio identification

Audio identification consist in the ability to pair audio signals of the same perceptual nature. In other words, the aim is to be able to compare an audio signal with a modified versions perceptually equivalent. To accomplish that, an audio fingerprint is extracted from the signals and only the fingerprints are compared to asses the similarity. Some guarantee have to be given about the equivalence between comparing audio fingerprints and perceptually comparing the signals. In designing AFPs, a dense representation is more robust than a sparse one. A dense representation also imply more compute cycles and hence a slower processing speed. To speedup the computing of a very dense audio fingerprint, able to stand stable under noise, re-recording, low-pass filtering, etc., we propose the use of a massive parallel architecture based on the Graphics Processing Unit (GPU) with the CUDA programming kit. We prove experimentally that even with a relatively small GPU and using a single core in the GPU, we are able to obtain a notable speedup per core in a GPU/CPU model. We compared our FFT implementation against state of the art CUFFT obtaining impressive results, hence our FFT implementation can help other areas of application.Presentado en el X Workshop Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

Fast GPU audio identification

Audio identification consist in the ability to pair audio signals of the same perceptual nature. In other words, the aim is to be able to compare an audio signal with a modified versions perceptually equivalent. To accomplish that, an audio fingerprint is extracted from the signals and only the fingerprints are compared to asses the similarity. Some guarantee have to be given about the equivalence between comparing audio fingerprints and perceptually comparing the signals. In designing AFPs, a dense representation is more robust than a sparse one. A dense representation also imply more compute cycles and hence a slower processing speed. To speedup the computing of a very dense audio fingerprint, able to stand stable under noise, re-recording, low-pass filtering, etc., we propose the use of a massive parallel architecture based on the Graphics Processing Unit (GPU) with the CUDA programming kit. We prove experimentally that even with a relatively small GPU and using a single core in the GPU, we are able to obtain a notable speedup per core in a GPU/CPU model. We compared our FFT implementation against state of the art CUFFT obtaining impressive results, hence our FFT implementation can help other areas of application.Presentado en el X Workshop Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

Unconditionally Secure Authentication and Integrity Protection for the Galileo Open Service Signal

The operational GNSSs do not offer authentication and integrity protection for the Open Service (OS) signal/message. But it is urgently needed, since several attacks can threat the OS user. By this reason the Galileo GNSS is working on this issue. This thesis contributes at the problem by adopting an approach as generic as possible, which outlines a theoretical bound on the key size. Therefore, the focus is providing data and signal unconditionally secure authentication and integrity pro

Topology control and data handling in wireless sensor networks

Our work in this thesis have provided two distinctive contributions to WSNs in the areas of data handling and topology control. In the area of data handling, we have demonstrated a solution to improve the power efficiency whilst preserving the important data features by data compression and the use of an adaptive sampling strategy, which are applicable to the specific application for oceanography monitoring required by the SECOAS project. Our work on oceanographic data analysis is important for the understanding of the data we are dealing with, such that suitable strategies can be deployed and system performance can be analysed. The Basic Adaptive Sampling Scheduler (BASS) algorithm uses the statistics of the data to adjust the sampling behaviour in a sensor node according to the environment in order to conserve energy and minimise detection delay. The motivation of topology control (TC) is to maintain the connectivity of the network, to reduce node degree to ease congestion in a collision-based medium access scheme; and to reduce power consumption in the sensor nodes. We have developed an algorithm Subgraph Topology Control (STC) that is distributed and does not require additional equipment to be implemented on the SECOAS nodes. STC uses a metric called subgraph number, which measures the 2-hops connectivity in the neighbourhood of a node. It is found that STC consistently forms topologies that have lower node degrees and higher probabilities of connectivity, as compared to k-Neighbours, an alternative algorithm that does not rely on special hardware on sensor node. Moreover, STC also gives better results in terms of the minimum degree in the network, which implies that the network structure is more robust to a single point of failure. As STC is an iterative algorithm, it is very scalable and adaptive and is well suited for the SECOAS applications

Synoptic analysis techniques for intrusion detection in wireless networks

Current system administrators are missing intrusion alerts hidden by large numbers of false positives. Rather than accumulation more data to identify true alerts, we propose an intrusion detection tool that e?ectively uses select data to provide a picture of ?network health?. Our hypothesis is that by utilizing the data available at both the node and cooperative network levels we can create a synoptic picture of the network providing indications of many intrusions or other network issues. Our major contribution is to provide a revolutionary way to analyze node and network data for patterns, dependence, and e?ects that indicate network issues. We collect node and network data, combine and manipulate it, and tease out information about the state of the network. We present a method based on utilizing the number of packets sent, number of packets received, node reliability, route reliability, and entropy to develop a synoptic picture of the network health in the presence of a sinkhole and a HELLO Flood attacker. This method conserves network throughput and node energy by requiring no additional control messages to be sent between the nodes unless an attacker is suspected. We intend to show that, although the concept of an intrusion detection system is not revolutionary, the method in which we analyze the data for clues about network intrusion and performance is highly innovative

Authentication and Integrity Protection at Data and Physical layer for Critical Infrastructures

This thesis examines the authentication and the data integrity services in two prominent emerging contexts such as Global Navigation Satellite Systems (GNSS) and the Internet of Things (IoT), analyzing various techniques proposed in the literature and proposing novel methods. GNSS, among which Global Positioning System (GPS) is the most widely used, provide affordable access to accurate positioning and timing with global coverage. There are several motivations to attack GNSS: from personal privacy reasons, to disrupting critical infrastructures for terrorist purposes. The generation and transmission of spoofing signals either for research purpose or for actually mounting attacks has become easier in recent years with the increase of the computational power and with the availability on the market of Software Defined Radios (SDRs), general purpose radio devices that can be programmed to both receive and transmit RF signals. In this thesis a security analysis of the main currently proposed data and signal level authentication mechanisms for GNSS is performed. A novel GNSS data level authentication scheme, SigAm, that combines the security of asymmetric cryptographic primitives with the performance of hash functions or symmetric key cryptographic primitives is proposed. Moreover, a generalization of GNSS signal layer security code estimation attacks and defenses is provided, improving their performance, and an autonomous anti-spoofing technique that exploits semi-codeless tracking techniques is introduced. Finally, physical layer authentication techniques for IoT are discussed, providing a trade-off between the performance of the authentication protocol and energy expenditure of the authentication process

Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks