No description available.

Satellite constellation design has been a well-studied problem since the beginning of the space age. In recent years new concepts and approaches tried to solve it with fewer satellites whilst guaranteeing coverage to the areas of interest, whether globally or regionally. This thesis introduces a novel approach based on the repeating ground track concept. It then links and converts the constellation design problem to a Set Cover problem. Although it is NP-hard, the Greedy Algorithm is capable to approximate the solution in a polynomial time with a logarithm ratio. An application of the non-symmetric strategy is illustrated with in 36 different scenarios, where altitude, sensor swath and time requirement are varied. In addition to that, a comparison with the Walker constellation on 6 scenarios is analyzed and discussed. In most cases the non-symmetric strategy produces constellations with significantly less satellites required. / Satellitkonstellationsdesign har varit ett väl studerat problem sedan början av rymdåldern. Under de senaste åren har nya koncept och tillvägagångssätt försökt lösa det med färre satelliter samtidigt som de garanterar täckning till intresseområdena, globalt eller regionalt. Detta examensarbete introducerar ett nytt tillvägagångssätt baserat på konceptet med återkommande markspår. Den länkar sedan och konverterar konstellationsdesignproblemet till ett Set Cover-problem. Även om problemet är NP-hårt, är den giriga algoritmen kapabel att approximera lösningen under polynomtid med ett logaritmförhållande. En tillämpning av den icke-symmetriska strategin illustreras med i 36 olika scenarier, där höjd, sensorsvep och tidsbehov varieras. Utöver det analyseras och diskuteras en jämförelse med Walker-konstellationen på 6 scenarier. I de allra flesta fall producerar den icke-symmetriska strategin konstellationer med betydligt färre satelliter.

Background. With the increasing demand for cybersecurity, there is a growing interest in understanding cyber-attack surfaces and vectors. Security Operation Centers (SOCs) play a crucial role in defensive cybersecurity, and Security Informationand Event Management (SIEM) systems are used to monitor and analyze the security status of computer systems. However, SIEM systems face challenges such asdata overload and the need for effective data selection.Objectives. This research aims to develop a method for reducing the number ofsets of Indicators of Compromise (IOCs) processed by SIEM systems while maintaining maximum coverage. The objectives include conducting a literature review onIOCs processing and data reduction, preparing data from the Open Threat Exchange(OTX) platform, developing a method for minimizing IOCs sets, and evaluating theeffectiveness of the proposed solution.Methods. The evaluation of the methods is performed numerically using a FuzzyTable. The research also involves developing a mathematical model that describesthe relationships between different types of IOCs and the possibility of various representations for the same object. The model takes into account weight assignmentto each indicator. Software implementation is carried out. The effectiveness of thedeveloped method is evaluated using metrics such as the coverage of the initial setof IOCs and the data reduction rateResults. Unfortunately, none of the methods fully met all the criteria. Fuzzy logicwas selected as the decision-making approach. A mathematical data model was developed to represent IOCs and associated pulses as sets. Dependencies were described tofilter out duplicate indicators. Implementation was done using the Python programming language. Three algorithms were implemented: Set cover problem, Weightedcoverage maximization, and Budget cover problem. Tests were conducted on theentire data set and subsets to analyze performance. The number of IOCs decreasedfrom 4115 to 3341, representing a reduction of 25.5% to 93% according to the Totaldata reduction metric. Conclusions. Overall, the developed method reduced information and minimizedindicator sources, offering a valuable approach for reducing data in IOC processing.

This paper deals with various approaches to solving optimization tasks. In prolog some examples from real life that show the application of optimization methods are given. Then term optimization task is defined and introducing of term fitness function which is common to all optimization methods follows. After that approaches by particle swarm optimization, ant colony optimization, simulated annealing, genetic algorithms and reinforcement learning are theoretically discussed. For testing we are using two discrete (multiple knapsack problem and set cover problem) and two continuous tasks (searching for global minimum of Ackley's and Rastrigin's function) which are presented in next chapter. Description of implementation details follows. For example description of solution representation or how current solutions are changed. Finally, results of measurements are presented. They show optimal settings for parameters of given optimization methods considering test tasks. In the end are given test tasks, which will be used for finding optimal settings of given approaches.