Rapporten beskriver utvecklingen av en webbutik för ett fiktivt företag inom arbets- och skyddsklädesbranschen. Detta projekt utgör examinationsmaterial för en kandidatexamen för gruppens medlemmar. Projektgruppen bestod av fem medlemmar som under projektets gång arbetat enligt Scrum-metodiken. Programmeringsspråken som används är Python, Javascript, HTML, CSS och SQL. En marknadsundersökning gjordes som bekräftade att marknad finns och identifierade lämpliga målgrupper. Efter detta skapades en produktvision med mål och krav för den slutliga produkten, varpå en produktbacklogg utvecklades för att på bästa sätt uppfylla denna vision. Projektet delades därefter in i fyra mindre delar, sprintar, för att få tydligare kortsiktiga mål och göra arbetet mer lättstrukturerat.När planeringen av arbetet var färdigt påbörjades en utvecklade fas där gruppen programmerade en webbutik efter de kriterier som fanns i produktbackloggen. Programmeringen skedde simultant på gruppens fem datorer och sammanfogades med jämna mellanrum på en dator, för att laddas upp i molntjänsten Google drive som den aktuella versionen. En fullt fungerande webbutik stod färdig efter 12 veckors utvecklande och finns att beskåda på http://skoab-tungdesign.rhcloud.com/.Efter att webbutiken var fullt fungerande inleddes en förbättringsfas där koden effektiviserades och standardiserades utan att det synliga i webbutiken förändrades. En testplan togs fram varefter en väsentlig mängd tänkbara scenarion testkördes och avvikelser dokumenterades och åtgärdades. Den slutliga produkten är en fullt fungerande webbutik bortsett från kortbetalningen där ingen riktig betalning sker av skäl angivna i rapporten. Produkten uppfyller alla de mål och krav som specificerats.

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The Internet traffic volume continues to grow at a great rate, now driven by video and TV distribution. For network operators it is important to avoid congestion in the network, and to meet service level agreements with their customers.  This thesis presents work on two methods operators can use to reduce links loads in their networks: traffic engineering and content caching. This thesis studies access patterns for TV and video and the potential for caching.  The investigation is done both using simulation and by analysis of logs from a large TV-on-Demand system over four months. The results show that there is a small set of programs that account for a large fraction of the requests and that a comparatively small local cache can be used to significantly reduce the peak link loads during prime time. The investigation also demonstrates how the popularity of programs changes over time and shows that the access pattern in a TV-on-Demand system very much depends on the content type. For traffic engineering the objective is to avoid congestion in the network and to make better use of available resources by adapting the routing to the current traffic situation. The main challenge for traffic engineering in IP networks is to cope with the dynamics of Internet traffic demands. This thesis proposes L-balanced routings that route the traffic on the shortest paths possible but make sure that no link is utilised to more than a given level L. L-balanced routing gives efficient routing of traffic and controlled spare capacity to handle unpredictable changes in traffic.  We present an L-balanced routing algorithm and a heuristic search method for finding L-balanced weight settings for the legacy routing protocols OSPF and IS-IS. We show that the search and the resulting weight settings work well in real network scenarios.

As we become more digitalized and we rely more heavily on the internet, the more important it is to protect ourselves against phishing attacks and other types of internet frauds. Users who fall for phishing attacks risk getting sensitive information stolen such as their bank accounts. In this thesis we describe and analyze domains that use Hypertext Transfer Protocol Secure (HTTPS), an extension to the Hypertext Transfer Protocol (HTTP) used for secure communication, and the impact that these domains have on phishing. We have analyzed and performed experiments that quantify how many of the phishing domains reported to PhishTank are HTTP and HTTPS, and why phishing sites can use HTTPS and still fail to be safe. We have created a script in Java that takes a set of URLs and creates a dataset containing the domains and all certificates that have been issued to these domains, making it a useful tool to analyze phishing domains. Furthermore, we present analyses and results describing how hashing algorithms are used in different certificates and their impact in securing the web. Through analyses and experiments we gained an understanding of how easy it is to create a certificate and claim to be behind a website. Phishing domains being able to use HTTPS is a good example of this and our results have shown that many imposter websites use HTTPS. Thankfully, there are tools in place to secure the web and avoid phishing, such as browsers having a set of Certificate Authorities (CAs) that they trust, meaning that any HTTPS site that does not have a certificate from one of these CAs will be flagged as not secure. Another countermeasure is increasing people's knowledge about how to handle websites that seem to be secure and have the necessary parameters, such as HTTPS, but nevertheless are phishing sites.

Fewer customers in Sweden are using cash in their everyday transactions than ever before. If this trend continues, then the Swedish payment system will, in a few years, be entirely controlled by private companies. Therefore the central bank needs a new digital asset trading platform that can replace the reliance on private companies with a system supplied by a government entity (central bank). This thesis revolves around the creation of a digital asset trading platform focused on the capital market, which can serve that role. The primary focus of the thesis is to investigate how time can be introduced to a blockchain so that events such as a coupon payment or a dividend can be scheduled to occur at a specific time. The digital trading platform created as part of this thesis was after creation tested to ascertain the best method of introducing time. The results presented in this thesis show that one of the methods has a higher accuracy, with a 1.3 seconds average between the desired execution time and the actual execution time. The platform was also used to evaluate the feasibility of a digital "currency" based on blockchains, as a replacement for credit cards supplied by Mastercard or Visa. The results indicate that a blockchain solution is a somewhat feasible replacement while suffering from some disadvantages, primarily in throughput.

An era is rising where more embedded systems are being moved to the edge. Everything from automated vehicles to smartphones with more complex machine learning architectures needs to be provided. Hence, the requirements of contributing with efficiency emerge more. Not only is this required for offline applications, but the demand is also rising for real-time applications. Therefore, software developers that are experts in real-time machine learning architecture may be in a situation where this architecture needs to be implemented on an embedded system that can provide the efficiency that is being demanded.FPGAs can provide with these demands. However, it is time-consuming to implement hardware description language (HDL) if not an expert. Our project has a main focus on building a frontend tool that gen-erates a dataflow programming language called CAL from a cus-tomized implementation for a specific machine learning model. The dataflow programming language CAL is used to accomplish an ef-ficient generation of hardware circuits. In this project, our primary focus is latency.The execution time of a software implementation has been compared to a hardware implementation where a Raspberry Pi 3b has con-tributed with the software implementation. A design space explo-ration has been done where different designs from the same model have been analyzed. In addition, the modules have also been ana-lyzed separately. In the analysis, latency is the factor explored.Results present a much faster execution time on the hardware im-plementation than the software implementation. Final results demon-strate a lower overall delay for modules implemented in parallel over modules implemented in serial. A parallel implementation reduced the overall delay with 242%.

People counting and human detection systems have during the last decade been developed using many different sensors; from video cameras to radars. While video cameras function much like the human eyes and yield data relatable to humans, it might be prone to privacy regulations. Therefore, sensors not affected by these privacy regulations have been used to develop people counting systems. In this thesis, radar sensors are examined in their people counting ability. The radars in question are: InnoSent iSYS5005, RFbeam K-LD7, and Acconeer XM112. An investigation of the three different radars as well as different geometries is carried out in order to find the most suitable radar and the optimal positioning for people counting. This thesis follows the principle of Design Science Research, where a controlled experiment was conducted in order to answer the research questions. We look at accuracy and precision together with other variables to determine which radar and what geometry is the most promising. In the end, we find that the iSYS5005 has the most promising result. However, the results were not as promising as one could have hoped, showing an overall poor performance. We argue that this is because the radar regularly loses track of objects, lowering the overall accuracy and precision. Which in turn lowers the overall performance of the sensors. Therefore, we mean that this shows the importance of post processing algorithms to interpret the data. We propose a tardiness probability algorithm which we believe could fix many of the issues the unprocessed data has.

Ever since the General Data Protection Regulation (GDPR) came into force, data anonymization has become even more relevant and important than before, especially for companies wanting to share and analyze their data. This thesis will anonymize smart meter data received from Tekniska Verken and evaluate how the anonymization method performs regarding the trade-off between information loss and privacy for the given dataset. The smart meter data consists of both personal data and the energy consumption recorded at a frequency of every hour. In the thesis, the given dataset is clustered with k-means, dividing the data into different groups depending on the daily energy patterns of each household. K-anonymity is then applied to each of the clusters to anonymize the data. The results show that the method used in this study had better privacy than utility. Additionally, when compared to anonymizing the dataset without clustering it, the method provided a better utility with less privacy protection.  Further, two datasets were created with a low re-identification risk to comply with the GDPR. However, it is difficult to say whether the two datasets complied with the GDPR, as it is essentially a decision to be made in a court case.

Detta arbete har som syfte att visa vad administrationsverktyget i Authentic Examination System kan förbättra för att uppnå högre effektivitet. För att hitta vad som kan förbättras utifrån effektivitet har metoden Think a loud använts tillsammans med mätvärden som ofta används för att mäta effektivitet. En prototyp har också utvecklats där hänsyn har tagits till de delar som hittats under analysen av administrationsverktyget. Prototypen och administrationsverktyget har sedan jämförts för att se hur de potentiella förbättringar som implementerats i prototypen påverkat effektiviteten. Slutligen sammanställs ettresultat som pekar på vad som kan effektiviseras i administrationsverktyget.

The advent of trends such as virtualization, cloud computing, IoT and BYOD has increased the traffic loads on modern enterprise and data-center networks. As the requirements on today’s networks increase, newer designs and solutions have sprout forth. Software-Defined Networking was developed to cater to the needs of modern networks and to improve traffic handling among other things. This study focuses on the ways SDN, specifically the OpenFlow standard, can be used to load-balance and increase the network throughput, in comparison to traditional methods such as Equal-Cost Load-Balancing. This was done by creating a test environment with the network emulator Mininet, and by creating load-balancing programs. The load-balancers were created using the OpenFlow protocol. These programs were used together with the Floodlight controller and were compared in the same environment. The results showed that the bandwidth load-balancer outperformed the Equal-Cost Load-Balancer.