Master's thesis "Innovation processes personal data for the state organization," deals with the protection of personal data for processing, both in paper, as well as electronic form. The main topic is security policy, the assessment of security risks resulting proposals for the introduction of innovative processes aimed to prevent possible leakage and misuse of personal data.

In this thesis, I present analysis of security incident reports in IDEA format from Mentat and their aggregation and correlation methods design and implementation. In data analysis, I show huge security reports diversity. Next, I show design of simple framework and system of templates. This framework and system of templates simplify aggregation and correlation methods design and implementation. Finally, I evaluate designed methods using Mentat database dumps. The results showed that designed methods can reduce the number of security reports up to 90% without loss of any significant information.

La qualité d'alimentation électrique d'un réseau ferroviaire peut être fortement affectée par l'indisponibilité d'un équipement électrique, que ce soit suite à un incident technique ou une opération de maintenance. Il est alors nécessaire de réduire le trafic prévu en ajustant les grilles horaires et les profils de vitesse, tout en conservant des performances d'exploitation optimales. Le but du travail présenté dans ce mémoire est de développer un outil d'aide à la décision pour assister les agents en charge de la réorganisation du trafic lors d'un incident sur infrastructure électrique. Le système étudié est complexe et son analyse repose sur des simulations coûteuses. Nous avons donc proposé une démarche en deux phases. Dans un premier temps, une analyse de sensibilité permet de détecter de manière efficace les variables d’ajustement du trafic les plus influentes. Après une analyse comparative entre différentes techniques, nous avons retenu l’analyse de sensibilité régionale par filtrage de Monte Carlo et test de KS, car cela permet de prendre en compte les contraintes opérationnelles, comme les niveaux de tension en ligne. La deuxième phase consiste à optimiser la solution en travaillant dans un espace de recherche de dimension réduite. Un ensemble de solutions Pareto optimales sont générées afin d’évaluer le meilleur compromis entre le critère principal qui est la densité de trafic et d’autres critères tels que les pertes ou les échauffements. Les techniques mises en œuvre ont abouti à la réalisation d’un prototype. Cet outil permet à l’ingénieur de définir les variables d’ajustement et les critères de performance du trafic. Il analyse ensuite l’influence des différentes variables d’ajustement et optimise le trafic par rapport aux critères définis. L’outil a été testé sur quatre cas d’étude correspondant à des portions de réseaux et à des trafics ferroviaires réels. / The power supply quality of a railway network can be strongly affected by the unavailability of electrical equipment, whether due to a technical incident or a maintenance operation. It is then necessary to reduce the expected traffic by adjusting the time schedules and speed profiles, while maintaining optimal operating performance. The purpose of the work presented in this thesis is to develop a decision support tool to assist the agents in charge of the reorganization of traffic during an incident on electrical infrastructure. The studied system is complex and its analysis is based on costly simulations. We therefore proposed a two-phase approach. As a first step, a sensitivity analysis can effectively detect the most influential traffic adjustment variables. After a comparative analysis between different techniques, we selected the regional sensitivity analysis by Monte Carlo filtering and KS test, because it allows us to take into account the operational constraints, like the tension levels in line. The second phase consists in optimizing the solution by working in a small research area. A set of Pareto-Optimal solutions are generated to evaluate the best trade-off between the main criterion "traffic density" and other criteria such as losses or overheating. The techniques implemented led to the production of a prototype. The tool allows the engineer to define traffic adjustment variables and traffic performance criteria. Then it analyzes the influence of the various adjustment variables and optimizes the traffic according to the defined criteria. The tool was tested on four case studies proposed by SNCF Réseau and corresponding to network segments and actual rail traffic.

La thèse traite des rapports entre documentations individuelle et communautaire des enseignants de mathématiques. L'étude est conduite sur deux terrains contrastés. Le premier est constitué d'une communauté institutionnelle : des enseignants de mathématiques d'un lycée, dont les classes sont simultanément équipées d'une technologie complexe ; le deuxième est constitué d'une communauté associative : un groupe de travail de l'association Sésamath engagé dans la conception d'un manuel numérique pour la classe de seconde. Dans les deux cas, l'étude s'intéresse plus particulièrement à l'enseignement de l'analyse, du fait de l'importance et de la complexité de ce domaine au niveau du lycée. La thèse propose des concepts (visions, monde du professeur, monde de la communauté) et des développements méthodologiques pour saisir les documentations individuelle et communautaire dans leur structure, leur dynamique et leurs interactions. Elle met en évidence, sur les deux terrains d'étude, des moments critiques de ces processus, les incidents documentaires, qui apparaissent à la fois comme des révélateurs et des accélérateurs. Elle montre enfin le potentiel qu'ont ces incidents pour le développement des articulations, globales ou locales, des documentations individuelles et communautaires / The present thesis treats the relation between the individual and community documentation of mathematics teachers. The study was carried out in two contrasting fields. The first field consists of an institutional community: mathematics teachers of a high school, whose classrooms are simultaneously equipped with complex technology; the second field consists of an associative community: a working group of Sésamath association that designs a digital Textbook for the grade 10. In the two cases, we are interested in the teaching of calculus, because of the importance and complexity of this mathematical field in high school. The thesis proposes new theoretical concepts (visions, teacher's world, and community world) and methodological developments to capture individual and community documentations in their structure, their dynamics and their interactions. It highlights, in the two fields of study, critical moments of these processes, documentary incidents, which appear as both revealing and accelerators. It demonstrates the potential these incidents have to develop articulations, global or local, between individual and community documentations

In this 7.5 HEC B-level thesis in Computer Science, a service desk web application is designed for Swedish government agencies with integrated major incident reporting functionality to the Swedish Civil Contingencies Agency (Myndigheten för samhällsskydd och beredskap). There are several advantages to integrating the major incident reporting procedure into the regular incident management process - information would no longer have to be duplicated, and the problems of untraceability and under-reporting could additionally be solved. The proof-of-concept application was designed and partially developed with ASP.NET Core (MVC) web framework and Bootstrap front-end framework. The user interface was evaluated with heuristic evaluation by the author and two master’s students in Information Security at Luleå Technical University. Although the proposed software and interface design may have left room for improvement, it did highlight the societal need for an efficient and responsible incident management process and the general benefits of integration.

Bakgrund: Att jobba i den stressfulla och komplexa miljö som tillhör intensiv- och akutsjukvårdens vardag har visat sig medföra större risker för att utveckla arbetsrelaterad stress och negativa känslomässiga reaktioner. En sjuksköterskas förmåga att hantera sina egna känslor påverkar direkt omvårdnaden och relationen till patienter och deras familjer. För att bibehålla en god psykisk hälsa och välbefinnande är det viktigt för sjuksköterskor att utveckla och använda effektiva strategier för att hantera negativa känslomässiga reaktioner efter svåra patientmöten på intensivvårdsavdelningar. Syfte: Syftet var att beskriva specialistsjuksköterskors strategier för att hantera negativa känslomässiga reaktioner efter svåra patientmöten på intensivvårdsavdelningar. Metod: Designen för arbetet gjordes som en kvalitativ studie med induktiv ansats. Sjutton specialistsjuksköterskor inom intensivvård och anestesi intervjuades med individuella semistrukturerade intervjuer. Metoden för att analysera data var kritisk incidentteknik. Resultat: Analysen av data resulterade i fem slutkategorier. Specialistsjuksköterkors strategier för att hantera negativa känslomässiga reaktioner innefattade; Att prata om händelsen med andra, Att ta stöd i varandra och hjälpas åt, Att tillåta sig att visa känslor och känna med patienten, Att strukturera upp arbetet, fokusera på vad som ska göras och lita på sin kompetens samt, Att utöva egenvård och ta hand om sig själv Slutsats: Att hantera negativa känslomässiga reaktioner efter svåra patientmöten på intensivvårdsavdelningar har visat att det är avgörande för att upprätthålla en hög nivå av arbetsprestation och professionalism. Studien ger en djupare förståelse för hur olika strategier kan användas för att sjuksköterskan ska behålla en god hälsa och kunna ge en god omvårdnad. En nyckelfaktor var att prata om händelsen och en önskan om större stöd och uppmärksamhet inom ämnet från både verksamheten och sjuksköterskeutbildningens håll.

Spear phishing attacks pose an ongoing threat to organizational cybersecurity, requiring effective response measures. This study examines measures that can be implemented by Swedish organizations to respond to successful spear phishing attacks, focusing on technical solutions and cybersecurity frameworks. Through 14 semi-structured interviews with incident response teams and cybersecurity professionals, insights were gathered on the effectiveness of these measures as well as the challenges that may be faced in complying with them. The results indicate the presence of two primary response measures: technical solutions used during and after the successful attack. In addition, cybersecurity frameworks play a critical role in guiding organizations in countering successful spear phishing attacks. While the results provide valuable insight, their effectiveness varies depending on the challenges the organization may face in complying with measures. This study underscores the importance of comprehensive and effective measures to respond to successful spear phishing attacks and improve organizational resilience to evolving cyber threats.

Algae and cyanobacteria occur naturally in source waters and are known to cause extensive problems in the drinking water treatment industry. Cyanobacteria (especially Anabaena sp. and Microcystis sp.) are responsible for many water treatment problems in drinking water treatment works (DWTW) all over the world because of their ability to produce organic compounds like cyanotoxins (e.g. microcystin) and taste and odour compounds (e.g. geosmin) that can have an adverse effect on consumer health and consumer confidence in tap water. Therefore, the monitoring of cyanobacteria in source waters entering DWTW has become an essential part of drinking water treatment management. Managers of DWTW, rely heavily on results of physical, chemical and biological water quality analyses, for their management decisions. But results of water quality analyses can be delayed from 3 hours to a few days depending on a magnitude of factors such as: sampling, distance and accessibility to laboratory, laboratory sample turn-around times, specific methods used in analyses etc. Therefore the use of on-line (in situ) instruments that can supply real-time results by the click of a button has become very popular in the past few years. On-line instruments were developed for analyses like pH, conductivity, nitrate, chlorophyll-a and cyanobacteria concentrations. Although, this real-time (on-line) data has given drinking water treatment managers a better opportunity to make sound management decisions around drinking water treatment options based on the latest possible results, it may still be “too little, too late” once a sudden cyanobacterial bloom of especially Anabaena sp. or Microcystis sp. enters the plant. Therefore the benefit for drinking water treatment management, of changing the focus from real-time results to future predictions of water quality has become apparent. The aims of this study were 1) to review the environmental variables associated with cyanobacterial blooms in the Vaal Dam, as to get background on the input variables that can be used in cyanobacterial-related forecasting models; 2) to apply rule-based Hybrid Evolutionary Algorithms (HEAs) to develop models using a) all applicable laboratory-generated data and b) on-line measureable data only, as input variables in prediction models for harmful algal blooms in the Vaal Dam; 3) to test these models with data that was not used to develop the models (so-called “unseen data”), including on-line (in situ) generated data; and 4) to incorporate selected models into two cyanobacterial incident management protocols which link to the Water Safety Plan (WSP) of a large DWTW (case study : Rand Water). During the current study physical, chemical and biological water quality data from 2000 to 2009, measured in the Vaal Dam and the 20km long canal supplying the Zuikerbosch DWTW of Rand Water, has been used to develop models for the prediction of Anabaena sp., Microcystis sp., the cyanotoxin microcystin and the taste and odour compound geosmin for different prediction or forecasting times in the source water. For the development and first stage of testing the models, 75% of the dataset was used to train the models and the remaining 25% of the dataset was used to test the models. Boot-strapping was used to determine which 75% of the dataset was to be used as the training dataset and which 25% as the testing dataset. Models were also tested with 2 to 3 years of so called “unseen data” (Vaal Dam 2010 – 2012) i.e. data not used at any stage during the model development. Fifty different models were developed for each set of “x input variables = 1 output variable” chosen beforehand. From the 50 models, the best model between the measured data and the predicted data was chosen. Sensitivity analyses were also performed on all input variables to determine the variables that have the largest impact on the result of the output. This study have shown that hybrid evolutionary algorithms can successfully be used to develop relatively accurate forecasting models, which can predict cyanobacterial cell concentrations (particularly Anabaena sp. and Microcystis sp.), as well as the cyanotoxin microcystin concentration in the Vaal Dam, for up to 21 days in advance (depending on the output variable and the model applied). The forecasting models that performed the best were those forecasting 7 days in advance (R2 = 0.86, 0.91 and 0.75 for Anabaena[7], Microcystis[7] and microcystin[7] respectively). Although no optimisation strategies were performed, the models developed during this study were generally more accurate than most models developed by other authors utilising the same concepts and even models optimised by hill climbing and/or differential evolution. It is speculated that including “initial cyanobacteria inoculum” as input variable (which is unique to this study), is most probably the reason for the better performing models. The results show that models developed from on-line (in situ) measureable data only, are almost as good as the models developed by using all possible input variables. The reason is most probably because “initial cyanobacteria inoculum” – the variable towards which the output result showed the greatest sensitivity – is included in these models. Generally models predicting Microcystis sp. in the Vaal Dam were more accurate than models predicting Anabaena sp. concentrations and models with a shorter prediction time (e.g. 7 days in advance) were statistically more accurate than models with longer prediction times (e.g. 14 or 21 days in advance). The multi-barrier approach in risk reduction, as promoted by the concept of water safety plans under the banner of the Blue Drop Certification Program, lends itself to the application of future predictions of water quality variables. In this study, prediction models of Anabaena sp., Microcystis sp. and microcystin concentrations 7 days in advance from the Vaal Dam, as well as geosmin concentration 7 days in advance from the canal were incorporated into the proposed incident management protocols. This was managed by adding an additional “Prediction Monitoring Level” to Rand Waters’ microcystin and taste and odour incident management protocols, to also include future predictions of cyanobacteria (Anabaena sp. and Microcystis sp.), microcystin and geosmin. The novelty of this study was the incorporation of future predictions into the water safety plan of a DWTW which has never been done before. This adds another barrier in the potential exposure of drinking water consumers to harmful and aesthetically unacceptable organic compounds produced by cyanobacteria. / PhD (Botany), North-West University, Potchefstroom Campus, 2015

Algae and cyanobacteria occur naturally in source waters and are known to cause extensive problems in the drinking water treatment industry. Cyanobacteria (especially Anabaena sp. and Microcystis sp.) are responsible for many water treatment problems in drinking water treatment works (DWTW) all over the world because of their ability to produce organic compounds like cyanotoxins (e.g. microcystin) and taste and odour compounds (e.g. geosmin) that can have an adverse effect on consumer health and consumer confidence in tap water. Therefore, the monitoring of cyanobacteria in source waters entering DWTW has become an essential part of drinking water treatment management. Managers of DWTW, rely heavily on results of physical, chemical and biological water quality analyses, for their management decisions. But results of water quality analyses can be delayed from 3 hours to a few days depending on a magnitude of factors such as: sampling, distance and accessibility to laboratory, laboratory sample turn-around times, specific methods used in analyses etc. Therefore the use of on-line (in situ) instruments that can supply real-time results by the click of a button has become very popular in the past few years. On-line instruments were developed for analyses like pH, conductivity, nitrate, chlorophyll-a and cyanobacteria concentrations. Although, this real-time (on-line) data has given drinking water treatment managers a better opportunity to make sound management decisions around drinking water treatment options based on the latest possible results, it may still be “too little, too late” once a sudden cyanobacterial bloom of especially Anabaena sp. or Microcystis sp. enters the plant. Therefore the benefit for drinking water treatment management, of changing the focus from real-time results to future predictions of water quality has become apparent. The aims of this study were 1) to review the environmental variables associated with cyanobacterial blooms in the Vaal Dam, as to get background on the input variables that can be used in cyanobacterial-related forecasting models; 2) to apply rule-based Hybrid Evolutionary Algorithms (HEAs) to develop models using a) all applicable laboratory-generated data and b) on-line measureable data only, as input variables in prediction models for harmful algal blooms in the Vaal Dam; 3) to test these models with data that was not used to develop the models (so-called “unseen data”), including on-line (in situ) generated data; and 4) to incorporate selected models into two cyanobacterial incident management protocols which link to the Water Safety Plan (WSP) of a large DWTW (case study : Rand Water). During the current study physical, chemical and biological water quality data from 2000 to 2009, measured in the Vaal Dam and the 20km long canal supplying the Zuikerbosch DWTW of Rand Water, has been used to develop models for the prediction of Anabaena sp., Microcystis sp., the cyanotoxin microcystin and the taste and odour compound geosmin for different prediction or forecasting times in the source water. For the development and first stage of testing the models, 75% of the dataset was used to train the models and the remaining 25% of the dataset was used to test the models. Boot-strapping was used to determine which 75% of the dataset was to be used as the training dataset and which 25% as the testing dataset. Models were also tested with 2 to 3 years of so called “unseen data” (Vaal Dam 2010 – 2012) i.e. data not used at any stage during the model development. Fifty different models were developed for each set of “x input variables = 1 output variable” chosen beforehand. From the 50 models, the best model between the measured data and the predicted data was chosen. Sensitivity analyses were also performed on all input variables to determine the variables that have the largest impact on the result of the output. This study have shown that hybrid evolutionary algorithms can successfully be used to develop relatively accurate forecasting models, which can predict cyanobacterial cell concentrations (particularly Anabaena sp. and Microcystis sp.), as well as the cyanotoxin microcystin concentration in the Vaal Dam, for up to 21 days in advance (depending on the output variable and the model applied). The forecasting models that performed the best were those forecasting 7 days in advance (R2 = 0.86, 0.91 and 0.75 for Anabaena[7], Microcystis[7] and microcystin[7] respectively). Although no optimisation strategies were performed, the models developed during this study were generally more accurate than most models developed by other authors utilising the same concepts and even models optimised by hill climbing and/or differential evolution. It is speculated that including “initial cyanobacteria inoculum” as input variable (which is unique to this study), is most probably the reason for the better performing models. The results show that models developed from on-line (in situ) measureable data only, are almost as good as the models developed by using all possible input variables. The reason is most probably because “initial cyanobacteria inoculum” – the variable towards which the output result showed the greatest sensitivity – is included in these models. Generally models predicting Microcystis sp. in the Vaal Dam were more accurate than models predicting Anabaena sp. concentrations and models with a shorter prediction time (e.g. 7 days in advance) were statistically more accurate than models with longer prediction times (e.g. 14 or 21 days in advance). The multi-barrier approach in risk reduction, as promoted by the concept of water safety plans under the banner of the Blue Drop Certification Program, lends itself to the application of future predictions of water quality variables. In this study, prediction models of Anabaena sp., Microcystis sp. and microcystin concentrations 7 days in advance from the Vaal Dam, as well as geosmin concentration 7 days in advance from the canal were incorporated into the proposed incident management protocols. This was managed by adding an additional “Prediction Monitoring Level” to Rand Waters’ microcystin and taste and odour incident management protocols, to also include future predictions of cyanobacteria (Anabaena sp. and Microcystis sp.), microcystin and geosmin. The novelty of this study was the incorporation of future predictions into the water safety plan of a DWTW which has never been done before. This adds another barrier in the potential exposure of drinking water consumers to harmful and aesthetically unacceptable organic compounds produced by cyanobacteria. / PhD (Botany), North-West University, Potchefstroom Campus, 2015

Since social networks (SNs) have become a global phenomenon in almost every industry, including airlines and banking, their security has been a major concern to most stakeholders. Several security techniques have been invented towards this but information security awareness (hereafter “awareness”) remains the most essential amongst all. This is because users, an important component of awareness, are a big problem on the SNs regardless of the technical security implemented. For SNs to improve on their awareness techniques or even determine the effectiveness of these security techniques, many measurement and evaluation techniques are in place to ascertain that controls are working as intended. While some of these awareness measurement techniques are inexpensive, effective and efficient to some extent, they are all incident-driven as they are based on the occurrence of (an) incident(s). In addition, these awareness measurement techniques may not present a true reflection of awareness, since many cyber incidents are often not reported. Hence, they are generally adjudged to be post mortem and risk-permissive. These limitations are major and unacceptable in some industries such as insurance, airlines and banking, where the risk tolerance level is at its lowest. This study therefore aims to employ a technical method to develop a non-incident statistics approach of measuring awareness efforts. Rather than evaluating the effectiveness of awareness efforts by the success of attacks or occurrence of an event, password cracking is presented and implemented to proactively measure the impacts of awareness techniques in SNs. The research encompasses the development and implementation of an SN – sOcialistOnline, the literature review of the past related works, indirect observation (available information), survey (as a questionnaire in a quiz template), and statistical analysis. Consequently, measurement of awareness efforts is shifted from detective and corrective paradigms to preventive and anticipatory paradigms, which are the preferred information security approaches going by their proactive nature. / Engineering, Science & Technology / D. Phil (Computer Science)