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As cyber threats continue to evolve, it is becoming increasingly important for organizations to have a Security Operations Center (SOC) in place to effectively defend against them. However, building and maintaining a SOC can be a daunting task without clear guidelines, policies, and procedures in place. Additionally, most current SOC solutions used by organizations are outdated, lack key features and integrations, and are expensive to maintain and upgrade. Moreover, proprietary solutions can lead to vendor lock-in, making it difficult to switch to a different solution in the future.
To address these challenges, this thesis proposes a comprehensive SOC framework and an open-source SOC solution that provides organizations with a flexible and cost-effective way to defend against modern cyber threats. The research methodology involved conducting a thorough literature review of existing literature and research on building and maintaining a SOC, including using SOC as a service. The data collected from the literature review was analyzed to identify common themes, challenges, and best practices for building and maintaining a SOC.
Based on the data collected, a comprehensive framework for building and maintaining a SOC was developed. The framework addresses essential areas such as the scope and purpose of the SOC, governance and leadership, staffing and skills, technologies and tools, processes and procedures, service level agreements (SLAs), and evaluation and measurement. This framework provides organizations with the necessary guidance and resources to establish and effectively operate a SOC, as well as a reference for evaluating the service provided by SOC service providers.
In addition to the SOC framework, a modern open-source SOC solution was developed, which emphasizes several key measures to help organizations defend against modern cyber threats. These measures include real-time, actionable threat intelligence, rapid and effective incident response, continuous security monitoring and alerting, automation, integration, and customization. The use of open-source technologies and a modular architecture makes the solution cost-effective, allowing organizations to scale it up or down as needed.
Overall, the proposed SOC framework and open-source SOC solution provide organizations with a comprehensive and systematic approach for building and maintaining a SOC that is aligned with the needs and objectives of the organization. The open-source SOC solution provides a flexible and cost-effective way to defend against modern cyber threats, helping organizations to effectively operate their SOC and reduce their risk of security incidents and breaches.
Cloud computing has revolutionized the way businesses operate by providing them with access to scalable, cost-effective, and flexible IT resources. This technology has enabled businesses to store, manage, and process data more efficiently, leading to improved competitiveness and increased revenue. The purpose of this thesis is to explore the impacts of using cloud computing from a business perspective. The research employs both primary and secondary sources of data, including a literature review, interviews with employees who have more than 5 years of experience, a questionnaire, and observations from Billwerk+ company.
The findings of this research indicate that cloud computing has had a significant impact on businesses, providing them with cost savings, improved agility and flexibility, and enhanced access to data and applications. However, it has been revealed that the benefits of cloud computing for companies may vary according to the departments of the employees. The results of this research contribute to the existing body of knowledge on the topic of cloud computing and its impact on businesses. The findings of this thesis can be used by business owners, managers, technology professionals, and students to make informed decisions about the adoption and use of cloud computing technology.
In conclusion, this thesis provides a comprehensive understanding of the impacts of using cloud computing from a business perspective, highlighting the factors that companies consider when deciding to use cloud environments and the views from different departments. The results of this research will be valuable to a wide range of individuals interested in exploring the implications of cloud computing for businesses.
Wie können sich Unternehmen erfolgreich im Social-Media-Umfeld ihrer Kunden platzieren? Mit dieser Fragestellung beschäftigt sich die vorliegende Forschungsarbeit. Die sozialen Netzwerke bieten viele Möglichkeiten, um potenzielle und bestehende Kunden zu erreichen, bringen aber gleichzeitig verschiedene Herausforderungen mit sich. Der Fokus dieser Arbeit liegt auf der Plattform Instagram. Dabei wird der Nutzer fokussiert, denn der Trend im Marketing geht zur Customer Centricity.
Maßnahmen, die Nutzer zum Abonnieren animieren, können nicht eindeutig benannt werden. Sie sind individuell vom Nutzer, aber auch vom Unternehmen und den jeweiligen Inhalten abhängig. Die Probanden der Umfragenstichprobe verhalten sich nach eigener Angabe beim Konsum, der Interaktion und dem Abonnieren zurückhaltend. Lediglich 8,94 Prozent gaben an, sich regelmäßig Profile von Unternehmen anzuschauen. Noch seltener als der Konsum findet die Interaktion mit Unternehmensinhalten statt. Die Hürde zur Interaktion und dem Abonnieren ist scheinbar hoch. Die Ergebnisse zeigen, dass es keine signifikanten Unterschiede im Nutzungsverhalten zwischen den Geschlechtern, den Beschäftigungsarten oder dem Budget gibt. Das lässt darauf schließen, dass das Handeln individuell und von anderen Faktoren abhängig ist. Jedoch bestätigt dies auch, dass der Trend des Content Marketings und der Customer Centricity weiterhin eine wichtige Rolle spielen. Der Nutzer möchte individuell angesprochen werden. Dabei sollen die Inhalte sinnvoll und inspirierend sein sowie nicht werblich wirken. Es bleibt die größte Herausforderung für Unternehmen in das private Umfeld der Nutzer auf Instagram einzutreten, ohne als „störend“ empfunden zu werden. Aus diesen Gründen ist eine weitere Forschung im Bereich „Unternehmen auf Instagram“ nötig. Die Schattenseite dieses Themenbereichs ist die Schnelllebigkeit und die fehlende Transparenz, beispielsweise zum Instagram-Algorithmus. Die vorliegende Forschung dient lediglich einer ersten Einschätzung des Themas, die ein möglichst breites Bild der Wahrnehmung von Unternehmen auf Instagram sowie vom Beispiel-Unternehmen Deinzigartig abbilden soll.
Total Cost of Ownership (TCO) is a key tool to have a complete understanding of the costs associated with an investment, as it allows to analyze not only the initial acquisition costs, but also the long-term costs related to operation, maintenance, depreciation, and other factors. In the context of the cement industry, TCO is especially important due to the complexity of the production processes and the wide variety of components and machinery involved in the process.
For this reason, a TCO analysis for the cement industry has been conducted in this study, with the objective of showing the different components of the cost of production. This analysis will allow the reader to gain knowledge about these costs, in the industrial model will be to make informed decisions on the adoption of technologies and practices that will allow them to reduce costs in the long run and improve their operational efficiency.
In particular, this study pursues to give visibility to technologies and practices that enable the reduction of carbon emissions in cement production, thus contributing to the sustainability of industry and the protection of the environment. By being at the forefront of sustainability issues, the cement industry can contribute to the achievement of environmentally friendly technologies and enable the development of people and industry.
The Oxyfuel technology has been selected as a carbon capture solution for the cement industry due to its practical application, low costs, and practical adaptation to non-capture processes. The adoption of this technology allows for a significant reduction in CO2 emissions, which is a crucial factor in achieving sustainability in the cement manufacturing process.
Carbon capture storage technologies represent a high investment, although these technologies increase the cost of production, the application of Oxyfuel technology is one of the most economically viable as the cheapest technology per capture according to the comparison. However, this price increase is a technical advantage as the carbon capture efficiency of this technology reaches 90%. This level of efficiency leads to a decrease in taxes for the generation of CO2 emissions, making the cement manufacturing process sustainable.
The goal of this thesis is to thoroughly investigate the concepts of stand-alone and decarbonization of optical fiber networks. Because of their dependability, fast speed, and capacity, optical fiber networks are vital inmodern telecommunications. Their considerable energy consumption and carbon emissions, on the other hand, constitute a danger to global sustainability objectives and must be addressed.
The first section of the thesis presents a summary of the current state of optical fiber networks, their
components, and the energy consumption connected with them. This part also goes over the difficulties of lowering energy usage and carbon emissions while preserving network performance and dependability.
The second section of the thesis focuses on the stand-alone idea, which entails powering the optical fiber network with renewable energy sources and energy-efficient technology. This section investigates and explores the possibilities of renewable energy sources like solar and wind power to power the network. It also investigates energy-efficient technologies like virtualization and cloud computing, as well as their potential to minimize network energy usage.
The third section of the thesis focuses on the notion of decarbonization, which entails lowering carbon emissions linked with the optical fiber network. This section looks at various carbon-reduction measures, such as employing low-carbon energy sources and improving energy efficiency. It also covers the relevance of carbon offsets and the difficulties associated with adopting decarbonization measures in the context of optical fiber networks.
The fourth section of the thesis compares the ideas of stand-alone and decarbonization. It investigates the advantages and disadvantages of each strategy, as well as their potential to minimize energy consumption and carbon emissions in optical fiber networks. It also explores the difficulties in applying these notions as well as potential hurdles to their wider adoption.
Finally, the need of addressing the energy consumption and carbon emissions connected with optical fiber networks is emphasized in this thesis.
It outlines important obstacles and potential impediments to adopting these initiatives and gives insights into potential ways for decreasing them.
It also makes suggestions for further study in this area.
Mit der Corona-Krise ist die digitale Bildung sehr stark in den Vordergrund gerückt. damit einhergehend wurde verstärkt E-Learning eingesetzt. Meistens wurde allerdings das volle Potenzial von E-Learning Plattformen nicht genutzt. Häufig wurden solche Systeme als Dateiablagen, Wissensdatenbanken oder nur für die Kollaboration Fähigkeiten benutzt. Aber darüber hinaus besitzen E-Learning Plattformen die Fähigkeit, eine Learning-Experience für die Lernenden zu erzeugen.
Diese Thesis beschäftigt sich mit KI-basierten Empfehlungssystemen im E-Learning Bereich. Dabei werden wissenschaftliche Arbeiten anhand der Methodik analysiert, welche Programmiersprachen, Algorithmen und Bibliotheken verwendet werden. Abhängig davon werden verschiedene prototypische Empfehlungsprogramme erstellt, wobei verschiedene Algorithmen ausprobiert werden. Diese Algorithmen werden bereinigte Tabellendateien verarbeiten, welche zuvor aus Moodle generiert werden. Dadurch sollen dann konkrete Empfehlungen erzeugt werden, die anschließend evaluiert und bewertet werden. Anschließend wird ein Fazit gezogen, worauf unter anderem auf die Probleme der verschiedenen Algorithmen hingewiesen wird und in welcher Hinsicht man das Empfehlungssystem verbessern könnte.
Diese Arbeit beschäftigt sich mit der Einführung von Digital Asset Management Systemen in einem Unternehmen. Ein Digital Asset Management dient der Verwaltung von digitalen Dateien von besonderer Bedeutung, sogenannte digitale Assets. Meist handelt es sich dabei um Bilder, aber auch Videos, Textdokumente und viele andere Dateitypen können in einem solchen System verwaltet werden. Unternehmen verfolgen dabei in der Regel das Ziel, einen zentralen Speicherort für alle relevanten Assets zu schaffen, um von dort aus die Verteilung steuern und die Dateien aktuell halten zu können. Systeme dieser Art werden beispielsweise in Marketingabteilungen eingesetzt, um Markeninhalte zentral verwalten und verteilen zu können. Die Einführung eines solchen Systems erfordert aufgrund der damit verbundenen Komplexität ein strukturiertes Vorgehen. Hierzu wird in dieser Arbeit ein mehr phasiges Vorgehen vorgeschlagen, das zu einer erfolgreichen Einführung eines Digital Asset Management Systems führt. Darüber hinaus werden auf Basis dieses Phasenansatzes Erfolgsfaktoren identifiziert und vorgestellt, auf die bei der Einführung besonders zu achten ist. Die Fallstudie einer tatsächlichen Einführung aus der Praxis beleuchtet die theoretisch erarbeiteten Erkenntnisse nochmals aus praktischer Sicht und erlaubt Rückschlüsse auf das vorgestellte theoretische Vorgehen.
Licht war für die Menschheit schon immer ein Hilfsmittel zur Orientierung. Das Zusammenspiel zwischen hellen und schattierten Oberflächen macht eine räumliche Wahrnehmung erst möglich. Die Lokalisierung von Lichtquellen bietet darüber hinaus für zahlreiche Anwendungsfelder, wie beispielsweise Augmented Reality, ein großes Potential.
Das Ziel der vorliegenden Arbeit war es, ein neuronales Netzwerk zu entwickeln, welches mit Hilfe eines selbst generierten, synthetischen Datensatzes eine Lichtsetzung parametrisiert. Dafür wurden State-of-the-Art Netzwerke aus der digitalen Bildverarbeitung eingesetzt.
Zu Beginn der Arbeit mussten die Eigenschaften der Lichtsetzung extrahiert werden. Eine weitere fundamentale Anforderung war die Aufbereitung des Wissens von Deep Learning.
Für die Generierung des synthetischen Datensatzes wurde eigens ein Framework entwickelt, welches auf der Blender Engine basiert.
Anschließend wurden die generierten Bilder und Metadaten in einem abgewandelten VGG16- und ResNet50-Netz trainiert, validiert und evaluiert.
Eine gewonnene Erkenntnis ist, dass sich künstlich generierte Daten eignen um ein neuronales Netz zu trainieren. Des Weiteren konnte gezeigt werden, dass sich mit Hilfe von Deep Learning Lichtsetzungsparameter extrahieren lassen.
Eine weiterführende Forschungsaufgabe könnte mit dem vorgeschlagenen Ansatzdie Lichtinszenierung von Augmented Reality Anwendungen verbessern.
Organizations striving to achieve success in the long term must have a positive brand image which will have direct implications on the business. In the face of the rising cyber threats and intense competition, maintaining a threat-free domain is an important aspect of preserving that image in today's internet world. Domain names are often near-synonyms for brand names for numerous companies. There are likely thousands of domains that try to impersonate the big companies in a bid to trap unsuspecting users, usually falling prey to attacks such as phishing or watering hole. Because domain names are important for organizations for running their business online, they are also particularly vulnerable to misuse by malicious actors. So, how can you ensure that your domain name is protected while still protecting your brand identity? Brand Monitoring, for example, may assist. The term "Brand Monitoring" applies only to keep tabs on an organization's brand performance, reception, and overall online presence through various online channels and platforms [1]. There has been a rise in the need of maintaining one's domain clear of any linkages to malicious activities as the threat environment has expanded. Since attackers are targeting domain names of organizations and luring unsuspecting users to visit malicious websites, domain monitoring becomes an important aspect. Another important aspect of brand abuse is how attackers leverage brand logos in creating fake and phishing web pages. In this Master Thesis, we try to solve the problem of classification of impersonated domains using rule-based and machine learning algorithms and automation of domain monitoring. We first use a rule-based classifier and Machine Learning algorithms to classify the domains gathered into two buckets – "Parked" and "Non-Parked". In the project's second phase, we will deploy object detection models (Scale Invariant Feature Transform - SIFT and Multi-Template Matching – MTM) to detect brand logos from the domains of interest.
Even though the internet has only been there for a short period, it has grown tremendously. To- day, a significant portion of commerce is conducted entirely online because of increased inter- net users and technological advancements in web construction. Additionally, cyberattacks and threats have expanded significantly, leading to financial losses, privacy breaches, identity theft, a decrease in customers’ confidence in online banking and e-commerce, and a decrease in brand reputation and trust. When an attacker pretends to be a genuine and trustworthy institution, they can steal private and confidential information from a victim. Aside from that, phishing has been an ongoing issue for a long time. Billions of dollars have been shed on the global economy. In recent years, there has been significant progress in the development of phishing detection and identification systems to protect against phishing attacks. Phishing detection technologies frequently produce binary results, i.e., whether a phishing attempt was made or not, with no explanation. On the other hand, phishing identification methodologies identify phishing web- pages by visually comparing webpages with predetermined authentic references and reporting phishing together with its target brand, resulting in findings that are understandable. However, technical difficulties in the field of visual analysis limit the applicability of currently available solutions, preventing them from being both effective (with high accuracy) and efficient (with little runtime overhead). Here, we evaluate existed framework called Phishpedia. This hybrid deep learning system can recognize identity logos from webpage screenshots and match logo variants of the same brand with high precision. Phishpedia provides high accuracy with low run- time. Lastly, unlike other methods, Phishpedia does not require training on any phishing sam- ples whatsoever. Phishpedia exceeds baseline identification techniques (EMD, PhishZoo, and LogoSENSE), inaccurately detecting phishing pages in lengthy testing using accurate phishing data. The effectiveness of Phishpedia was tested and compared against other standard machine learning algorithms and some state-of-the-art algorithms. The given solutions performed better than different algorithms in the given dataset, which is impressive.