Fingerprints play an important role in biometrics and cryptography. Their creation might be based on one-way hash functions, which should usually also be collision-resistant. But users tend to draw less attention at those fingerprints - so an attacker might hand out a similar fingerprint in order to spoof identity. The main ideas for creating such 'fuzzy fingerprints' and the creation algorithm itself are discussed in this lecture. The demonstration of the tool, that produces fuzzy fingerprints shows the practical background of this technique. / Fingerabdrücke besitzen sowohl in der Kryptographie als auch in der Biometrie eine große Bedeutung. In kryptographischen Anwendungen werden diese durch Einweg-Hash-Verfahren erzeugt, die für bestimmte Anwendungen auch kollisionsresitent sein müssen. In der Praxis schenken Benutzer diesen Fingerprints weit weniger Aufmerksamkeit - oft genügt es nur hinreichend ähnliche Fingerprints auszugeben, um die Nutzer zu täuschen Die Kriterien, die dabei erfüllt sein müssen und die Erzeugung dieser "Fuzzy Fingerprints" sind Hauptbestandteil dieses Vortrags. Durch die Demonstration eines Tools im praktischen Einsatz wird dieser abgeschlossen.

(EN) This dissertation thesis contains scientific results achieved in the field of analytical chemistry, particularly liquid chromatography. The major part of the results has been published in prestigious international journals in five papers. In addition to that, relevant yet unpublished results have been included as well. In general terms, the work presented here contributed to the concerted efforts to tackle the current lack of novel antibiotics. Specifically, high-performance liquid chromatography (HPLC) and ultra high-performance liquid chromatography (UHPLC) techniques coupled to a variety of detection systems have been employed for analysis of antibiotics and actinomycete secondary metabolites. The first thematic part describes the development of liquid chromatography methods for analysis of lincomycin precursors, lincomycin precursor analogues, and lincomycin derivatives. The methods have been applied to study lincomycin biosynthetic pathway and obtain improved lincomycin derivatives by mutasynthesis. The second thematic part aims at investigating alternative approaches for analysis of antibiotics. Firstly, the core-shell particle and the sub-2 μm particle chromatographic columns were compared. The core-shell particle columns compatible with HPLC proved to be a convenient alternative to the...

Context: Indoor positioning systems have become more widespread over the past decade, mainly due to devices such as Bluetooth Low Energy beacons which are low at cost and work effectively. The context of this thesis is to localize and help people navigate to the office equipment, meeting rooms, etc., in an office environment using machine learning algorithms. This can help the employees to work more effectively and conveniently saving time. Objective: To perform a literature review of various machine learning models in indoor positioning that are suitable for an office environment. Also, to experiment with those selected models and compare the results based on their performance. Android smartphone and BLE beacons have been used to collect RSSI values along with their respective location coordinates for the dataset. Besides, the accuracy of positioning is determined by using state-of-the-art machine learning algorithms to train the dataset. Using performance metrics such as Euclidean distance error, CDF curve of Euclidean distance error, RMSE and MAE to compare results and select the best model for this research. Methods: A Fingerprinting method for indoor positioning is studied and applied for the collection of the RSSI values and (x, y) location coordinates from the fixed beacons. A literature review is performed on various machine learning models appropriate for indoor positioning. The chosen models were experimented and compared based on their performances using performance metrics such as CDF curve, MAE, RSME and Euclidean distance error. Results: The literature study shows that Long Short Term Memory and Multi-layer perceptron, Gradient boosting, XG boosting and Ada boosting is suitable for models for indoor positioning. The experimentation and comparison of these models show that the overall performance of Long short-term memory network was better than multiplayer Perceptron, Gradient boosting, XG boosting and Adaboosting. Conclusions: After analysing the acquired results and taking into account the real-world scenarios to which this thesis is intended, it can be stated that the LSTM network provides the most accurate location estimation using beacons. This system can be monitored in real-time for maintenance and personnel tracking in an office environment.

Multi-factor authentication (MFA) offers a wide range of methods and techniques available today. The security benefits of using MFA are almost indisputable, however, users are reluctant to adopt the technology. While many new MFA solutions are being proposed, there is a lack of consideration for user sentiment in the early stages of development. In an attempt to balance security and usability, this report investigates the feasibility of a new authentication mechanism that uses browser fingerprinting, graphical passwords, and honeytokens. This was evaluated by conducting a limited literature review, producing a prototype, interviews with test users, and security experts, as well as ensuring feasibility through a requirements checklist. The results of this research provides evidence that this mechanism is feasible, and appealing to end users. However, more investigation is required in order to ensure the mechanism's viability in a real-world deployment.

This Master’s thesis studies different ways of exploiting the signal strength measurements from wireless interfaces for position determination. Difficulties include handling the fluctuating observations and their sensitivity to obstruction. We list important factors to take into account before describing a new system based on location fingerprinting and capable of integrating observations from multiple wireless interfaces. Compared to typical fingerprinting solutions, the training time is an order of magnitude shorter, but the location resolution is limited to locations of particular interest. In an office environment, the proposed solution determines the location correctly 80 percent of the time with sufficient precision for being used with context-aware services. In an open space environment, an incorrect location is reported 42 percent of the time. / Det här exjobbet studerar olika sätt att använda signalstyrka från trådlösa gränssnitt för positionsbestämning. Några av svårigheterna ligger i att hantera observationernas fluktuationer och deras känslighet för obstruktion. De viktigaste faktorerna att ta hänsyn till tas upp innan ett nytt system beskrivs. Det är baserat på positionsigenkänning (location fingerprinting) och kan dra nytta av observationer från flera olika trådlösa gränssnitt. Jämfört med vanliga metoder för positionsigenkänning är träningstiden en storleksordning kortare, men positionsupplösningen är begränsad till ett visst antal positioner av särskilt värde. I en kontorsmiljö klarar den föreslagna lösningen att korrekt bestämma positionen i 80 procent av fallen med tillräckligt hög noggrannhet för att användas till kontextmedvetna tjänster (context-aware services). I en öppen rumslösning ger lösningen en felaktig position i 42 procent av fallen.

The demand for accurate localization under indoor environments has increased dramatically in recent years. To be cost-effective, most of the localization solutions are based on the WiFi signals, utilizing the pervasive deployment of WiFi infrastructure and availability of the WiFi enabled mobile devices. In this thesis, we develop completed indoor localization solutions based on WiFi fingerprinting and machine learning approaches with two types of WiFi fingerprints including received signal strength indicator (RSSI) and channel state information (CSI). Starting from the low complexity algorithm, we propose a soft range limited K nearest neighbours (SRL-KNN) to address spatial ambiguity and the fluctuation of WiFi signals. SRL-KNN exploits RSSI and scales the fingerprint distance by a range factor related to the physical distance between the user’s previous position and the reference location in the database. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Besides, the idea of the soft range limiting factor can be applied to all of the existed probabilistic methods, i.e., parametric and nonparametric methods, to improve their performances. A semi-sequential short term memory step is proposed to add to the existed probabilistic methods to reduce their spatial ambiguity of fingerprints and boost significantly their localization accuracy. In the following research phase, instead of locating user's position one at a time as in the cases of conventional algorithms, our recurrent neuron networks (RNNs) solution aims at trajectory positioning and takes into account of the relation among RSSI measurements in a trajectory. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. Next, the problem of localization using only one single router is analysed. CSI information will be adopted along with RSSI to enhance the localization accuracy. Each of the reference point (RP) is presented by a group of CSI measurements from several WiFi subcarriers which we call CSI images. The combination of convolutional neural network (CNN) and LSTM model is proposed. CNN extracts the useful information from several CSI values (CSI images), and then LSTM will exploit this information in sequential timesteps to determine the user's location. Finally, a fully practical passive indoor localization is proposed. Most of the conventional methods rely on the collected WiFi signal on the mobile devices (active information), which requires a dedicated software to be installed. Different from them, we leverage the received data of the routers (passive information) to locate the position of the user. The localization accuracy is investigated through experiments with several phones, e.g., Nexus 5, Samsung, Iphone and HTC, in hundreds of testing locations. The experimental results demonstrate that our proposed localization scheme achieves an average localization error of around 1.5 m when the phone is in idle mode, and approximately 1 m when it actively transmits data. / Graduate

L'assurance de la sécurité sanitaire des aliments vis-à-vis des contaminants chimiques est un enjeu en constante évolution en raison des sources multiples de contamination (pesticides, mycotoxines, néoformés indésirables, et migrants des matériaux au contact entre autres). Actuellement, l'évaluation complète de la qualité sanitaire d'un aliment nécessite la multiplication de méthodes analytiques dites « ciblées » ayant un coût important. De plus, malgré la multiplication des méthodes ciblées, tout contaminant non recherché ne sera pas détecté. Il apparaît nécessaire aujourd'hui de faire évoluer ces méthodes vers des approches analytiques « non-ciblées » susceptibles, via l'analyse d'empreintes chimiques, d'évaluer la présence d'une gamme aussi large que possible des contaminants dans une matrice alimentaire. Les travaux de thèse ont porté sur l'utilisation de la spectrométrie de masse haute résolution (LC-HRMS) et de la chimiométrie pour développer une méthode capable de caractériser la qualité sanitaire des aliments. La matrice de développement est le thé, choisi pour sa complexité d’analyse, sa large consommation et les alertes sanitaires récurrentes à son sujet. Une première preuve de concept de la méthode a été mise en place sur un thé vert de référence et un panel de 32 contaminants choisis pour leur diversité de sources et structures chimiques, puis des situations plus complexes ont été investiguées : application à d’autres types de thé, analyse simultanée d’échantillons de marques et d’origines géographiques distinctes, et enfin application en aveugle à des situations de contamination complexes avec la présence de plusieurs schémas de contamination au sein du même jeu d’échantillons. L’utilisation d’outils de traitement de données libres et ouverts a permis de développer un processus de traitement des données unifié pour deux plateformes analytiques LC-HRMS de technologies et de marques différentes (ToF et Orbitrap), ce qui n’a jamais été réalisé pour l’étude de la sécurité sanitaire chimique des aliments. Par ailleurs, le développement de ce processus a été l’occasion de réaliser une étude méthodologique du comportement de certains outils pour les approches non-ciblées de détection des contaminants de l’aliment / Ensuring food safety, especially toward chemical contaminants, is an issue in constant evolution due to multiple sources of contamination (pesticides, mycotoxins, neoformed contaminants, migrants from packaging among others). Currently, several targeted analyses are needed to fully assess the chemical safety of foods, generating high cost. Moreover, despite the number of analyses performed, a contaminant not targeted is not detected. Therefore, it is necessary to develop new methods based on non-targeted approaches able to assess, through analysis of chemical fingerprints, the occurrence of as many contaminants as possible in a food matrix. The main purpose of this work lies in the use of high resolution mass spectrometry (LC-HRMS) and chemometrics in order to develop a method capable of assessing food safety. Tea has been chosen as a development product for its analytical complexity, its broad consumption and its safety issues. A first proof-of-concept of the method has been set up on a reference green tea with a pool of 32 representative food contaminants, chosen for their diversity in terms of sources and chemical structures. More complex situations were further investigated with different types of tea, several brands considered at once and, last but not least, with the application to blind detection of contaminants in complex cases. Free and open-source data analysis tools were used to build a unified data treatment process to analyze data from two LC-HRMS analytical platforms of different technologies (ToF and Orbitrap), which is new for food safety studies. The development of this process also enabled a methodological study of the behavior of several tools used in untargeted approaches for food safety.

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Schweine im Bestand und auf dem Schlachthof, Schlachttierkörper und Lebern sowie Hackfleisch vom Schwein und Schweinefleisch aus dem Handel wurden auf das Vorkommen von thermophilen Campylobacter spp. untersucht. Zusätzlich wurden Putenkarkassen auf einem Putenschlachthof und Hähnchen- und Putenfleischerzeugnisse aus dem Handel mit in die Untersuchung einbezogen. Die in den Proben gefundenen Campylobacter-Isolate wurden zuerst phänotypisch charakterisiert, anschließend erfolgte eine genotypische Feindifferenzierung durch zwei molekularbiologische Fingerprintingmethoden (AFLP-Typisierung und FLA-Typing). Durch den Vergleich der Isolate untereinander und mit humanen Campylobacter-Isolaten sollten epidemiologische Zusammenhänge geklärt und die Bedeutung von Geflügel- und Schweinefleisch als Infektionsquelle für den Menschen aufgezeigt werden. / Faeces of pigs at the farm and the slaughterhouse, pig carcasses, livers, minced meat and porc from retail were investigated for the occurence of thermophilic Campylobacter spp. Turkey carcasses at a turkey slaughterhouse and chicken and turkey products from retail were also included in this investigation. First the Campylobacter isolates found in the samples were characterized phenotypically, afterwards they were typed with two molecularbiological fingerprinting methods (AFLP- and FLA-Typing). The comparison of the isolates with human isolates should give answers to epidemiological questions and the importance of poultry and porc as a source of infection for humans.