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- Fakultät Medien (M) (ab 22.04.2021) (165) (remove)
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Linux and Linux-based operating systems have been gaining more popularity among the general users and among developers. Many big enterprises and large companies are using Linux for servers that host their websites, some even require their developers to have knowledge about Linux OS. Even in embedded systems one can find many Linux-based OS that run them. With its increasing popularity, one can deduce the need to secure such a system that many personnel rely on, be it to protect the data that it stores or to protect the integrity of the system itself, or even to protect the availability of the services it offers. Many researchers and Linux enthusiasts have been coming up with various ways to secure Linux OS, however new vulnerabilities and new bugs are always found, by malicious attackers, with every update or change, which calls for the need of more ways to secure these systems.
This Thesis explores the possibility and feasibility of another way to secure Linux OS, specifically securing the terminal of such OS, by altering the commands of the terminal, getting in the way of attackers that have gained terminal access and delaying, giving more time for the response teams and for forensics to stop the attack, minimize the damage, restore operations, and to identify collect and store evidence of the cyber-attack. This research will discuss the advantages and disadvantages of various security measures and compare and contrast with the method suggested in this research.
This research is significant because it paints a better picture of what the state of the art of Linux and Linux-based operating systems security looks like, and it addresses the concerns of security enthusiasts, while exploring new uncharted area of security that have been looked at as a not so significant part of protecting the OSes out of concern of the various limitations and problems it entails. This research will address these concerns while exploring few ways to solve them, as well as addressing the ideal areas and situations in which the proposed method can be used, and when would such method be more of a burden than help if used.
Die vorliegende Bachelorarbeit befasst sich mit den Marketing-Kommunikationsstrategien im zweiseitigen Markt am Beispiel einer Immobilienplattform.
Das primäre Ziel dieser Arbeit besteht darin, auf Grundlage der Netzwerkeffekte im zweiseitigen Markt und durch Anwendung von Onlinemarketingmaßnahmen die Positionierung der Plattform Immobilien Rith in den Internetsuchergebnissen zu optimieren.
On a regular basis, we hear of well-known online services that have been abused or compromised as a result of data theft. Because insecure applications jeopardize users' privacy as well as the reputation of corporations and organizations, they must be effectively secured from the outset of the development process. The limited expertise and experience of involved parties, such as web developers, is frequently cited as a cause of risky programs. Consequently, they rarely have a full picture of the security-related decisions that must be made, nor do they understand how these decisions affect implementation accurately.
The selection of tools and procedures that can best assist a certain situation in order to protect an application against vulnerabilities is a critical decision. Regardless of the level of security that results from adhering to security standards, these factors inadvertently result in web applications that are insufficiently secured. JavaScript is a language that is heavily relied on as a mainstream programming language for web applications with several new JavaScript frameworks being released every year.
JavaScript is used on both the server-side in web applications development and the client-side in web browsers as well.
However, JavaScript web programming is based on a programming style in which the application developer can, and frequently must, automatically integrate various bits of code from third parties. This potent combination has resulted in a situation today where security issues are frequently exploited. These vulnerabilities can compromise an entire server if left unchecked. Even though there are numerous ad hoc security solutions for web browsers, client-side attacks are also popular. The issue is significantly worse on the server side because the security technologies available for server-side JavaScript application frameworks are nearly non-existent.
Consequently, this thesis focuses on the server-side aspect of JavaScript; the development and evaluation of robust server-side security technologies for JavaScript web applications. There is a clear need for robust security technologies and security best practices in server-side JavaScript that allow fine-grained security.
However, more than ever, there is this requirement of reducing the associated risks without hindering the web application in its functionality.
This is the problem that will be tackled in this thesis: the development of secure security practices and robust security technologies for JavaScript web applications, specifically, on the server-side, that offer adequate security guarantees without putting too many constraints on their functionality.
Though the basic concept of a ledger that anyone can view and verify has been around for quite some time, today’s blockchains bring much more to the table including a way to incentivize users. The coins given to the miner or validator were the first source of such incentive to make sure they fulfilled their duties. This thesis draws inspiration from other peer efforts and uses this same incentive to achieve certain goals. Primarily one where users are incentivised to discuss their opinions and find scientific or logical backing for their standpoint. While traditional chains form a consensus on a version of financial "truth", the same can be applied to ideological truths too. To achieve this, creating a modified or scaled proof of stake consensus mechanism is explored in this work. This new consensus mechanism is a Reputation Scaled - Proof of Stake. This reputation can be built over time by voting for the winning side consistently or by sticking to one’s beliefs strongly. The thesis hopes to bridge the gap in current consensus algorithms and incentivize critical reasoning.
The progress in machine learning has led to advanced deep neural networks. These networks are widely used in computer vision tasks and safety-critical applications. The automotive industry, in particular, has experienced a significant transformation with the integration of deep learning techniques and neural networks. This integration contributes to the realization of autonomous driving systems. Object detection is a crucial element in autonomous driving. It contributes to vehicular safety and operational efficiency. This technology allows vehicles to perceive and identify their surroundings. It detects objects like pedestrians, vehicles, road signs, and obstacles. Object detection has evolved from being a conceptual necessity to an integral part of advanced driver assistance systems (ADAS) and the foundation of autonomous driving technologies. These advancements enable vehicles to make real-time decisions based on their understanding of the environment, improving safety and driving experiences. However, the increasing reliance on deep neural networks for object detection and autonomous driving has brought attention to potential vulnerabilities within these systems. Recent research has highlighted the susceptibility of these systems to adversarial attacks. Adversarial attacks are well-designed inputs that exploit weaknesses in the deep learning models underlying object detection. Successful attacks can cause misclassifications and critical errors, posing a significant threat to the functionality and safety of autonomous vehicles. With the rapid development of object detection systems, the vulnerability to adversarial attacks has become a major concern. These attacks manipulate inputs to deceive the target system, significantly compromising the reliability and safety of autonomous vehicles. In this study, we focus on analyzing adversarial attacks on state-of-the-art object detection models. We create adversarial examples to test the models’ robustness. We also check if the attacks work on a different object detection model meant for similar tasks. Additionally, we extensively evaluate recent defense mechanisms to see how effective they are in protecting deep neural networks (DNNs) from adversarial attacks and provide a comprehensive overview of the most commonly used defense strategies against adversarial attacks, highlighting how they can be implemented practically in real-world situations.
Durch ein wachsendes Bewusstsein für soziale und nachhaltige Konflikte spielt verantwortungsvolles unternehmerisches Handeln eine zunehmend große Rolle für potenzielle Kund*innen. Viele Unternehmen entscheiden sich daher für Corporate-Social-Responsibility-Marketing: Sie greifen wichtige gesellschaftliche Themen in ihren Kampagnen auf. Nicht immer steckt dahinter ein edles Motiv. Wirtschaftliches Interesse steht häufig über ethischem Antrieb. Doch in einigen Fällen durchschauen Kund*innen diese Irreführung.
Ziel dieser wissenschaftlichen Arbeit ist die Analyse der Auswirkungen von unglaubwürdigem Corporate-Social-Responsibility-Marketing auf die Kaufbereitschaft von Studierenden. Die Untersuchung wird anhand des Unternehmens Nike durchgeführt. Hierfür wird den Proband*innen zunächst ein Corporate-Social-Responsibility-Werbespot von Nike gezeigt. Anschließend wird ihnen ein Skandal präsentiert, der eben diesen Werbespot als reine Marketing-Masche enttarnt. Zwischenzeitlich wird die Kaufbereitschaft der Studierenden mit einem Fragebogen gemessen. Zusätzlich werden die Teilnehmer*innen zu ihren Gedanken und Gefühlen befragt, die Kampagne und Skandal hinterlassen.
Aus dem Experiment ging hervor, dass die Teilnehmer*innen nach der Präsentation des Skandals eine signifikant niedrigere Kaufbereitschaft zeigten. Außerdem wurde ein leicht erhöhtes Interesse an Nikes Tätigkeiten als Sponsor ermittelt. Mehr als die Hälfte der Befragten äußerten zudem ihre Enttäuschung und einen veränderten Blick auf das Unternehmen Nike.
Much of the research in the field of audio-based machine learning has focused on recreating human speech via feature extraction and imitation, known as deepfakes. The current state of affairs has prompted a look into other areas, such as the recognition of recording devices, and potentially speakers, by only analysing sound files. Segregation and feature extraction are at the core of this approach.
This research focuses on determining whether a recorded sound can reveal the recording device with which it was captured. Each specific microphone manufacturer and model, among other characteristics and imperfections, can have subtle but compounding effects on the results, whether it be differences in noise, or the recording tempo and sensitivity of the microphone while recording. By studying these slight perturbations, it was found to be possible to distinguish between microphones based on the sounds they recorded.
After the recording, pre-processing, and feature extraction phases we completed, the prepared data was fed into several different machine learning algorithms, with results ranging from 70% to 100% accuracy, showing Multi-Layer Perceptron and Logistic Regression to be the most effective for this type of task.
This was further extended to be able to tell the difference between two microphones of the same make and model. Achieving the identification of identical models of a microphone suggests that the small deviations in their manufacturing process are enough of a factor to uniquely distinguish them and potentially target individuals using them. This however does not take into account any form of compression applied to the sound files, as that may alter or degrade some or most of the distinguishing features that are necessary for this experiment.
Building on top of prior research in the area, such as by Das et al. in in which different acoustic features were explored and assessed on their ability to be used to uniquely fingerprint smartphones, more concrete results along with the methodology by which they were achieved are published in this project’s publicly accessible code repository.
Entwicklung eines Kommunikationskonzepts für Black Forest Formula an der Hochschule Offenburg
(2021)
Die vorliegende Masterthesis befasst sich mit der Entwicklung eines Kommunikationskonzepts für das Black Forest Formula Projekt an der Hochschule Offenburg. Die bisherigen kommunikativen Aktivitäten werden im Rahmen dieser Arbeit überarbeitet und erweitert, sodass ein konsistentes Gesamtkonzept für die Kommunikation entsteht. Hierzu werden die einzelnen Phasen eines Konzeptionsprozesses durchlaufen. Das Ziel ist eine langfristige Planung, welche die Interessen der verschiedenen Gruppen innerhalb und außerhalb des Projekts berücksichtigt, um deren Unterstützung zu sichern. Des Weiteren wird durch die Ausrichtung auf festgelegte Kommunikationsziele eine Verbesserung der internen und externen Kommunikation von Black Forest Formula angestrebt. Die Arbeit richtet sich daher insbesondere an die leitenden Mitglieder des Teams und soll diesen als Handlungsempfehlung dienen. Darüber hinaus ist die Arbeit auch für all jene interessant, die sich mit dem Ablauf und den Methoden zur Entwicklung von Kommunikationskonzepten befassen möchten.
Bilder und Filme sind aus dem modernen Leben nicht mehr wegzudenken. Besonders die Präsenz im Internet – die Webseite der Firma, der persönliche Blog oder der Auftritt in sozialen Medien – kommt nicht mehr ohne die richtige Visualisierung aus. Wer entsprechende Fähigkeiten und Ausrüstung besitzt, erstellt sich seine benötigten Inhalte selbst. Wer diese Möglichkeit nicht hat, muss auf Werke anderer zurückgreifen. Einfach aus dem Internet herunterladen und benutzen darf man diese allerdings nicht, denn sie sind in den meisten Fällen urheberrechtlich geschützt. Große Firmen und Personen mit entsprechendem Budget buchen für diese Zwecke deshalb extra Fotografen oder kaufen ihre gewünschten Inhalte auf beliebten Stock-Plattformen wie Shutterstock oder Getty Images. Diese Abschlussarbeit richtet sich an diejenigen, die das nicht können: (Werk-)Studenten im Bereich Medien, Gestaltung oder Onlinemarketing sowie kleinere Firmen und Einzelpersonen, welche Bilder und Filme für private, schulische, redaktionelle oder kommerzielle Zwecke benötigen.
Ziel dieser Arbeit ist das Vorstellen der zahlreichen Möglichkeiten, im Internet Bilder und Filme für die kostenlose und rechtssichere Nutzung zu recherchieren. Dabei werden verschiedene Arten der kommerziellen und nichtkommerziellen Nutzung berücksichtigt, sodass möglichst viele Leser einen Nutzen aus dieser Abschlussarbeit ziehen können. Hierzu wird zunächst die Rechtslage in Deutschland geklärt und anschließend 7 Plattformen sowie 2 Suchmaschinen für Bilder und 7 Plattformen für Filme untersucht und miteinander verglichen. Für jede Plattform werden ihre typischen Features vorgestellt und ihre Bedeutung für die Recherche aufgezeigt. Dies soll als Hilfestellung für einen effizienten Recherche-Arbeitsablauf auf der jeweiligen Plattform dienen. Anschließend wird die jeweilige Bild- und Videoauswahl anhand beispielhafter Suchbegriffe dargestellt.