Background. Mass impregnated high voltage cables are used in, for example, submarine electric power transmission. One of the production steps of such cables is the lapping process in which several hundred layers of special purpose paper are wrapped around the conductor of the cable. It is important for the mechanical and electrical properties of the finished cable that the paper is applied correctly, however there currently exists no reliable way of continuously ensuring that the paper is applied correctly. Objective. The objective of this thesis is to develop a prototype of a cost-effective machine vision system which monitors the lapping process and detects and records any errors that may occur during the process; with an accuracy of at least one tenth of a millimetre. Methods. The requirements of the system are specified and suitable hardware is identified. Using a method where the images are projected down to one axis as well as other signal processing methods, the errors are measured. Experiments are performed where the accuracy and performance of the system is tested in a controlled environment. Results. The results show that the system is able to detect and measure errors accurately down to one tenth of a millimetre while operating at a frame rate of 40 frames per second. The hardware cost of the system is less than €200. Conclusions. A cost-effective machine vision system capable of performing measurements accurate down to one tenth of a millimetre can be implemented using the inexpensive Raspberry Pi 3 and Raspberry Pi Camera Module V2. Th

Background. Archiving of legacy information systems is challenging. When no options exist for extracting the information in a structured way, the last resort is to save the database. Optimally only the information that is relevant should be saved and the rest of the information could be removed. Objectives. The goal is to develop a method for assisting the archivist in the process of culling a database before archiving. The method should be described as rules defining how the tables can be identified.Methods. To get an overview of how the process works today and what archivists think can be improved, a number of interviews with experts in database archiving is done. The results from the interviews are then analysed, together with test databases to define rules that can be used in a general case. The rules are then implemented in a prototype that is tested and evaluated to verify if the method works. Results. The results point to the algorithm being both faster and able to exclude more irrelevant tables than a person could do with the manual method. An algorithm for finding candidate keys has also been improved to decrease the number of tests and execution time in the worst case. Conclusions. The evaluation shows results that point to the method working as intended while resulting in less work for the archivist. More work should be done on this method to improve it further.

Context. Random Forests (RFs) is a very popular machine learning algorithm for mining large scale data. RFs is mainly known asan algorithm that operates in offline mode. However, in recent yearsimplementations of online random forests (ORFs) have been introduced. With multicore processors and successful implementation ofparallelism may result in increased performance of an algorithm, inrelation to its sequential implementation. Objectives. In this paper we develop and investigate the performanceof a parallel implementation of ORFs and compare the experimentalresults with its sequential counterpart. Methods. From using profiling tools on ORFs we located its bottlenecks and with this knowledge we used the implementation/experiment methodology to develop parallel online random forests (PORFs).Evaluation is done by comparing performance from ORFs and PORFs. Results. Experiments on common machine learning data sets showthat PORFs achieve equal classification to our execution of ORFs. However, there is a difference in classification on some data sets whencompared to results from another study. Furthermore, PORFs didn’tachieve any speed up compared to ORFs. In fact with the added overhead from pthreads PORFs takes longer time to finish than ORFs. Conclusions. We conclude that our parallelization of ORFs achievesequal classification performance as sequential ORFs. However, speedup wasn’t achieved with our chosen approach for parallelism. Possible solutions to achieve speed up is presented and suggested as futurework.

Making predictions nowadays is of high importance for any company, whether small or large, as thanks to the possibility to analyze the data available, new market opportunities can be found, risks and costs can be reduced, among others. Machine learning algorithms for time series can be used for predicting future values of interest. However, choosing the appropriate algorithm and tuning its metaparameters require a great level of expertise. This creates an adoption barrier for small and medium enterprises which could not afford hiring a machine learning expert to their IT team. For these reasons, this project studies different possibilities to make good predictions based on machine learning algorithms, but without requiring great theoretical knowledge from the users. Moreover, a software package that implements the prediction process has been developed. The software is an ensemble method that first predicts a value taking into account different algorithms at the same time, and then it combines their results considering also the previous performance of each algorithm to obtain a final prediction of the value. Moreover, the solution proposed and implemented in this project can also predict according to a concrete objective (e.g., optimize the prediction, or do not exceed the real value) because not every prediction problem is subject to the same constraints. We have experimented and validated the implementation with three different cases. In all of them, a better performance has been obtained in comparison with each of the algorithms involved, reaching improvements of 45 to 95%.

The domain specific language P4 is a novel initiative which extends the Software-Defined Networking (SDN) paradigm by allowing for data plane programmability. Network virtualisation is a class of network technologies which can be used to abstract the addressing in a network, allowing multiple tenants to utilise the network resources while being agnostic to the underlying network and the other tenants. In other words, twins of tenants using the same addresses can co-exist on the same underlying network. If a twin is a distributed network, it may even be spread out across multiple sites which are connected to a common backbone. In this study, network virtualisation using P4 is evaluated with emphasis on scalability in terms of number of twins and sites. A set of potential network virtualisation technologies are identified and categorised. Based on this categorisation, two variations of network virtualisation are implemented on the P4 capable software switch BMv2 and the performance of both variations are evaluated against the non-P4 solution Linux bridge. Linux bridge was found to yield 451 times more useful bandwidth than the best performing P4 implementation on BMv2, while also learning MAC addresses faster and generating less traffic on the backbone. It is concluded that the performance of network virtualisation implemented and running on BMv2 is worse compared to the non-P4 solution Linux bridge.

IT-säkerhet är ett brett och viktigt område. IT-säkerhet är ett ständigt växande område och attveta att vi som konsumenter kan interagera med våra enheter och internet säkert är ytterstviktigt. Internet och molntjänster är något som ständigt används av både enstaka individer ochföretag. Företag använder molnet för deras dagliga verksamheter så väl som för att sälja derasegna tjänster.Det finns olika typer av molnservicemodeller, IaaS, SaaS och PaaS tjänster. Att veta vaddessa modeller innebär ger grunden till kunskapen om vad företag som erbjuder dessa tjänsterarbetar med, deras prioriteringar och vad de kan erbjuda. Som konsumenter använder vimånga typer av molntjänster, det är viktigt att konsumenten vet att företagen som hanterarpersonlig information och data gör det varsamt och professionellt.Detta arbete ämnar upplysa om frågan, hur företag hanterar och arbetar med säkerheten imolnet samt att förstå tankesättet och tekniken som företagen använder. För att uppnå dettamål utför författarna av detta arbete intervjuer med företag som arbetar med olikamolntjänster. Både företag som använder molnet för deras egna tjänster så väl som företagsom arbetar med molnet för att erbjuda tjänster. / IT security is a broad and important field. IT security is an always expanding field andknowing that we as consumers can interact with our devices, the internet or just anyconnected devices safely is very important. Most consumers, even companies interact daily,and extensively with the internet, and they interact with different types of cloud services.Companies can use the cloud to complete daily tasks as well as for selling their own services.There are different types of cloud service models, such as IaaS, SaaS and PaaS-services.These models are the basis for knowing what a company works with, their priorities and whatthey offer. We as consumers utilize many types of cloud services, it should be important forall of us to know that the people handling our personal information and data, do it with theutmost care and in a professional matter.This paper aims to answer the question of how companies handles and work with the securityof the cloud and to understand the reasoning and techniques involved, and to do this theauthors are interviewing different companies that work with different cloud services. Bothcompanies that utilize the cloud for their own services as well as companies that work withthe cloud, to offer services.

Cryptocurrencies are gaining prominence among individuals and companies alike, resulting in the growing adoption of so-called cryptocurrency wallet applications, as these simplify carrying out transactions. These wallets are available in a myriad of different forms and specifications. For example, there are hardware and software wallets. The latter can be divided into mobile, web, and desktop wallets. All of them offer attackers various ways to exploit vulnerabilities and steal money from victims. It is hard to keep track of this multitude of options and thus choose the right cryptocurrency wallet. For this reason, in this thesis, we collect the findings from previous literature to provide an overview of the various attack surfaces, possible countermeasures, and further research. Our systematic study has shown that there is still a considerable variety of attack vectors, which we have divided into six subcategories, (i) Memory and Storage, (ii) Operating Systems, (iii) Software Layer, (iv) Network Layer, (v) Blockchain Protocol, and (vi) Others. Some of the identified vulnerabilities have currently no solutions at all or hardly applicable countermeasures. However, some are easy to fix and simple to implement. One of the essential measures is to raise awareness of the identified weaknesses and the associated mitigations, if any, among the involved stakeholders, including users, developers, and exchanges.

No description available.

In the past, industrial control systems (ICS) and supervisory control and data acquisition (SCADA) systems were planned to run as isolated networks, and not interconnect with other networks e.g., the internet or other parts of a corporate’s network. Because of the isolation, no cybersecurity mechanism was required. In the modern society, ICS/SCADA systems has evolved to communicate over public IP networks and has been incorporated in a company’s intranet or directly to the internet. This integration opens up for threats that were not envisioned at the time when the system was created. When ICS/SCADA systems get exposed to the internet, there is a risk that vulnerabilities in the systems get exploited by a malicious force. This can lead to data loss, destruction of data and devices, damage to infrastructure, financial losses for the company, and even loss of human life could occur. To mitigate and prevent attacks it is crucial to understand the attacks and the behaviour of the attacker. One way to achieve this is setting up a system that mimics the real system. This fake system is separated from the production network and closely monitored. The data collected can be analysed and used to gain knowledge about the attacks. This thesis will present a possible way to study attacks on an ICS/SCADA system using a honeypot designed for this purpose. To do this, a suitable honeypot had to be found that could collect relevant data regarding what kind of attacks that may be used against an ICS/SCADA system. This was achieved by experimenting with different set ups, and the collected data was analysed. This led to the use of T-pot as the chosen honeypot and the collected data showed that a lot of the traffic were directed towards the ICS/SCADA communication protocols Modbus and s7comm. To secure an ICS/SCADA system, it is important to gain knowledge about attacks and attack vectors. A honeypot can be a useful tool that provide information regarding attacks and attackers and can be a help in setting up a defence-in-depth strategy to improve the security in an ICS/SCADA network.

No description available.