Air traffic communication is one of the most vital systems for air traffic management controllers. It is used every day to allow millions of people to travel safely and efficiently across the globe. But many of the systems considered industry-standard are used without any sort of encryption and authentication meaning that they are vulnerable to different wireless attacks. In this thesis vulnerabilities within an air traffic management system called ADS-B will be investigated. The structure and theory behind this system will be described as well as the reasons why ADS-B is unencrypted. Two attacks will then be implemented and performed in an open-source air traffic management simulator called openScope. ADS-B data from these attacks will be gathered and combined with actual ADS-B data from genuine aircrafts. The collected data will be cleaned and used for machine learning purposes where three different algorithms will be applied to detect attacks. Based on our findings, where two out of the three machine learning algorithms used were able to detect 99.99% of the attacks, we propose that machine learning algorithms should be used to improve ADS-B security. We also think that educating air traffic controllers on how to detect and handle attacks is an important part of the future of air traffic management.

Purpose – The purpose of the study is to expand the knowledge of how wheel motor currents can be combined with machine learning to be used in a collision detection system for autonomous robots, in order to decrease the number of external sensors and open new design opportunities and lowering production costs. Method – The study is conducted with design science research where two artefacts are developed in a cooperation with Globe Tools Group. The artefacts are evaluated in how they categorize data given by an autonomous robot in the two categories collision and non-collision. The artefacts are then tested by generated data to analyse their ability to categorize. Findings – Both artefacts showed a 100 % accuracy in detecting the collisions in the given data by the autonomous robot. In the second part of the experiment the artefacts show that they have different decision boundaries in how they categorize the data, which will make them useful in different applications. Implications – The study contributes to an expanding knowledge in how machine learning and wheel motor currents can be used in a collision detection system. The results can lead to lowering production costs and opening new design opportunities. Limitations – The data used in the study is gathered by an autonomous robot which only did frontal collisions on an artificial lawn. Keywords – Machine learning, K-Nearest Neighbour, Multilayer Perceptron, collision detection, autonomous robots, Collison detection based on current. / Syfte – Studiens syfte är att utöka kunskapen om hur hjulmotorstömmar kan kombineras med maskininlärning för att användas vid kollisionsdetektion hos autonoma robotar, detta för att kunna minska antalet krävda externa sensorer hos dessa robotar och på så sätt öppna upp design möjligheter samt minska produktionskostnader Metod – Studien genomfördes med design science research där två artefakter utvecklades i samarbete med Globe Tools Group. Artefakterna utvärderades sedan i hur de kategoriserade kollisioner utifrån en given datamängd som genererades från en autonom gräsklippare. Studiens experiment introducerade sedan in data som inte ingick i samma datamängd för att se hur metoderna kategoriserade detta. Resultat – Artefakterna klarade med 100% noggrannhet att detektera kollisioner i den giva datamängden som genererades. Dock har de två olika artefakterna olika beslutsregioner i hur de kategoriserar datamängderna till kollision samt icke-kollisioner, vilket kan ge dom olika användningsområden Implikationer – Examensarbetet bidrar till en ökad kunskap om hur maskininlärning och hjulmotorströmmar kan användas i ett kollisionsdetekteringssystem. Studiens resultat kan bidra till minskade kostnader i produktion samt nya design möjligheter Begränsningar – Datamängden som användes i studien samlades endast in av en autonom gräsklippare som gjorde frontalkrockar med underlaget konstgräs. Nyckelord – Maskininlärning, K-nearest neighbor, Multi-layer perceptron, kollisionsdetektion, autonoma robotar

This thesis introduces readers various methods that are currently used for detection of network-based applications. Further part deals with selection of appropriate detection method and implementation of proof-of-concept script, including testing its reliability and accuracy. Chosen detection algorithm is based on statistics data from network flows of tested network communication. Due to its final solution does not depend on whether communication is encrypted or not. Next part contains several possible variants of how to integrate proposed solution in the current architecture of the existing product Kernun UTM --- which is firewall produced by Trusted Network Solutions a.s. company. Most suitable variant is chosen and described furthermore in more details. Finally there is also mentioned plan for further developement and possible ways how to improve final solution.

The National Library of Sweden uses optical character recognition software to digitize their collections of historical newspapers. The purpose of such software is first to automatically segment text and images from scanned newspaper pages, and second to read the contents of the identified text regions. While the raw text is often digitized successfully, important contextual information regarding whether the text constitutes for example a header, a section title or the body text of an article is not captured. These characteristics are easy for a human to distinguish, yet they remain difficult for a machine to recognize. The main purpose of this thesis is to investigate how well section titles in the newspaper Svenska Dagbladet can be classified by using so called image embeddings as features. A secondary aim is to examine whether section titles become harder to classify in older newspaper data. Lastly, we explore if manual annotation work can be reduced using the predictions of a semi-supervised classifier to help in the labeling process.  Results indicate the use of image embeddings help quite substantially in classifying section titles. Datasets from three different time periods: 1990-1997, 2004-2013, and 2017 and onwards were sampled and annotated. The best performing model (Xgboost) achieved macro F1 scores of 0.886, 0.936 and 0.980 for the respective time periods. The results also showed classification became more difficult on older newspapers. Furthermore, a semi-supervised classifier managed an average precision of 83% with only single section title examples, showing promise as way to speed up manual annotation of data.

Several studies have been conducted within machine learning, and various variations have been applied to a wide spectrum of other fields. However, a thorough feasibility study within the payment processing industry using machine learning classifier algorithms is yet to be explored. Here, we construct a rule-based response vector and use that in combination with a magnitude of varying feature vectors across different machine learning classifier algorithms to try and determine whether individual transactions can be considered profitable from a business point of view. These algorithms include Naive-Bayes, AdaBoosting, Stochastic Gradient Descent, K-Nearest Neighbors, Decision Trees and Random Forests, all helped us build a model with a high performance that acts as a robust confirmation of both the benefits and a theoretical guide on the implementation of machine learning algorithms in the payment processing industry. The results as such are a firm confirmation on the benefits of data intensive models, even in complex industries similar to Swedbank Pay’s. These Implications help further boost innovation and revenue as they offer a better understanding of the current pricing mechanisms. / Många studier har utförts inom ämnet maskininlärning, och olika variationer har applicerats på ett brett spektrum av andra ämnen. Däremot, så har en ordentlig genomförbarhetsstudie inom betalningsleveransindustrin med hjälp av klassificeringsalgortimer har ännu ej utforskats. Här har vi konstruerat en regelbaserad responsvektor och använt den, tillsammans med en rad olika och varierande egenskapvektorer på olika maskininlärningsklassificeringsalgoritmer för att försöka avgöra ifall individuella transaktioner är lönsamma utifrån företagets perspektiv. Dessa algoritmer är Naive-Bayes, AdaBoosting, Stokastisk gradient medåkning, K- Närmaste grannar, beslutsträd och slumpmässiga beslutsskogar. Alla dessa har hjälpt oss bygga en teoretisk vägledning om implementering av maskininlärningsalgoritmer inom betalningsleveransindustrin. Dessa resultat är en robust bekräftelse på fördelarna av dataintensiva modeller även inom sådana komplexa industrier Swedbank Pay är verksamma inom. Implikationerna hjälper vidare att förstärka innovationen och öka intäkterna eftersom de erbjuder en bättre förståelse för deras nuvarande prissättningsmekanism.

Sentiment Analysis is an area of Computer Science that deals with the impact a document makes on a user. The very field is further sub-divided into Opinion Mining and Emotion Analysis, the latter of which is the basis for the present work. Work on songs is aimed at building affective interactive applications such as music recommendation engines. Using song lyrics, we are interested in both supervised and unsupervised analyses, each of which has its own pros and cons. For an unsupervised analysis (clustering), we use a standard probabilistic topic model called Latent Dirichlet Allocation (LDA). It mines topics from songs, which are nothing but probability distributions over the vocabulary of words. Some of the topics seem sentiment-based, motivating us to continue with this approach. We evaluate our clusters using a gold dataset collected from an apt website and get positive results. This approach would be useful in the absence of a supervisor dataset. In another part of our work, we argue the inescapable existence of supervision in terms of having to manually analyse the topics returned. Further, we have also used explicit supervision in terms of a training dataset for a classifier to learn sentiment specific classes. This analysis helps reduce dimensionality and improve classification accuracy. We get excellent dimensionality reduction using Support Vector Machines (SVM) for feature selection. For re-classification, we use the Naive Bayes Classifier (NBC) and SVM, both of which perform well. We also use Non-negative Matrix Factorization (NMF) for classification, but observe that the results coincide with those of NBC, with no exceptions. This drives us towards establishing a theoretical equivalence between the two.

Cílem mé diplomové práce bylo navrhnout metodu analýzy retinálních sekvencí, která bude hodnotit kvalitu jednotlivých snímků. V teoretické části se také zabývám vlastnostmi retinálních sekvencí a způsobem registrace snímků z fundus kamery. V praktické části je implementována metoda hodnocení kvality snímků, která je otestována na reálných retinálních sekvencích a vyhodnocena její úspěšnost. Práce hodnotí i vliv této metody na registraci retinálních snímků.

In electronic warfare, one of the key technologies is radar. Radar is used to detect and identify unknown aerial, nautical or land-based objects. An attribute of of a pulsed radar signal is the Pulse Repetition Interval (PRI) which is the time interval between pulses in a pulse train. In a passive radar receiver system, the PRI can be used to recognize the emitter system. Correct classification of emitter systems is a crucial part of Electronic Support Measures (ESM) and Radar Warning Receivers (RWR) in order to deploy appropriate measures depending on the emitter system. Inaccurate predictions of emitter systems can have lethal consequences and variables such as time and confidence in the predictions are essential for an effective predictive method. Due to the classified nature of military systems and techniques, there are no industry standard systems or techniques that perform quick and accurate classifications of emitter systems based on PRI. Therefore, methods that allows for fast and accurate predictions based on PRI is highly desirable and worthy of research. This thesis explores and compares the capabilities of two machine learning methods for the task of classifying emitters based on received PRI. The first method is an attention based model which performs well throughout all levels of realistic noise and is quick to learn and even quicker to give accurate predictions. The second method is a K-Nearest Neighbor (KNN) implementation that, while performing well for noise-free PRI, finds its performance degrading as the amount of noise increases. An additional outcome of this thesis is the development of a system to generate samples in an automated fashion. The attention based model performs well, achieving a macro avarage F1-score of 63% in the 59-class recognition task whereas the performance of the KNN is lower, achieving a macro avarage F1-score of 43%. Future research could be conducted with the purpose of designing a better attention based model for producing higher and more confident predictions and designing algorithms to reduce the time complexity of the KNN implementation. / En av de viktigaste teknikerna inom telektrig är radarn. Radar används för att upptäcka och identifiera okända, luftburna, sjögående eller landbaserade förmål. En komponent av radar är Pulsrepetitionsinterval (Pulse Repetition Intervall, PRI) som beskrivs som tidsintervallet mellan två inkommande pulser. I ett radarvarnar system (Radar Warning Receiver, RWR) kan PRI användas för att identifiera radarsystem. Korrekt identifiering av radarsystem är en viktig uppgift för elektroniska understödsmedel (Electronic Support Measures, ESM) med syfte att tillsätta lämpliga medel beroende på radarsystemet i fråga. Icke tillförlitlig identifiering av radarsystem kan ha dödliga konsekvenser och variabler som tid och säkerhet i identifieringen är avgörande för ett effektivt system. Då dokumentation och specifikationer för militära system i regel är hemligstämplade är det svårt att utröna någon typ av industristandard för att utföra snabb och säker klassificering av radarsystem baserat på PRI. Därför är det av stort intresse detta område och möjligheterna för sådana lösningar utforskas. Detta examensarbete utforskar och jämför förmågorna hos två maskininlärningsmetoder i avseende att korrekt identifiera radarsändare baserat på genererat PRI. Den första metoden är ett djupt neuralt nätverk som använder sig av tekniken ”attention”. Det djupa nätverket presterar bra för alla brusnivåer och lär sig snabbt att känna igen attributen hos PRI som kännetecknar vilken radarsändare och som efter träning dessutom är snabb på att korrekt identifiera PRI. Den andra metoden är en K-Nearest Neighbor implementation som förvisso presterar bra på icke brusig data men vars förmåga försämras allt eftersom brusnivåerna ökar. Ett ytterligare resultat av arbetet är utvecklingen och implementationen av en metod för att specificera PRI och sedan generera PRI efter specifikation. Attention modellen genererar bra prediktioner för data bestående av 59 klasser, med ett F1-score snitt om 63% medan KNN-implementationen för samma uppgift har en lägre träffsäkerhet med ett F1-score snitt om 43%. Vidare forskning kan innefatta utökad utveckling av det djupa, neurala nätverket i syfte att förbättra dess förmåga för identifiering och metoder för att minimera tidsåtgången för KNN implementationen.