@phdthesis{Kilic2016,
  author    = {Kilic, Mukayil},
  title     = {Vernetztes Pr{\"u}fen von elektronischen Komponenten {\"u}ber das Internet},
  school      = {Universit{\"a}t Potsdam},
  pages     = {104, XVI},
  year      = {2016},
  language  = {de}
}
@phdthesis{Koehlmann2016,
  author    = {K{\"o}hlmann, Wiebke},
  title     = {Zug{\"a}nglichkeit virtueller Klassenzimmer f{\"u}r Blinde},
  publisher = {Logos},
  address   = {Berlin},
  isbn      = {978-3-8325-4273-3},
  pages     = {i-x, 310, i-clxxvi},
  year      = {2016},
  abstract  = {E-Learning-Anwendungen bieten Chancen f{\"u}r die gesetzlich vorgeschriebene Inklusion von Lernenden mit Beeintr{\"a}chtigungen. Die gleichberechtigte Teilhabe von blinden Lernenden an Veranstaltungen in virtuellen Klassenzimmern ist jedoch durch den synchronen, multimedialen Charakter und den hohen Informationsumfang dieser L{\"o}sungen kaum m{\"o}glich. Die vorliegende Arbeit untersucht die Zug{\"a}nglichkeit virtueller Klassenzimmer f{\"u}r blinde Nutzende, um eine m{\"o}glichst gleichberechtigte Teilhabe an synchronen, kollaborativen Lernszenarien zu erm{\"o}glichen. Im Rahmen einer Produktanalyse werden dazu virtuelle Klassenzimmer auf ihre Zug{\"a}nglichkeit und bestehende Barrieren untersucht und Richtlinien f{\"u}r die zug{\"a}ngliche Gestaltung von virtuellen Klassenzimmern definiert. Anschließend wird ein alternatives Benutzungskonzept zur Darstellung und Bedienung virtueller Klassenzimmer auf einem zweidimensionalen taktilen Braille-Display entwickelt, um eine m{\"o}glichst gleichberechtigte Teilhabe blinder Lernender an synchronen Lehrveranstaltungen zu erm{\"o}glichen. Nach einer ersten Evaluation mit blinden Probanden erfolgt die prototypische Umsetzung des Benutzungskonzepts f{\"u}r ein Open-Source-Klassenzimmer. Die abschließende Evaluation der prototypischen Umsetzung zeigt die Verbesserung der Zug{\"a}nglichkeit von virtuellen Klassenzimmern f{\"u}r blinde Lernende unter Verwendung eines taktilen Fl{\"a}chendisplays und best{\"a}tigt die Wirksamkeit der im Rahmen dieser Arbeit entwickelten Konzepte.},
  language  = {de}
}
@phdthesis{Makowski2021,
  author    = {Makowski, Silvia},
  title     = {Discriminative Models for Biometric Identification using Micro- and Macro-Movements of the Eyes},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xi, 91},
  year      = {2021},
  abstract  = {Human visual perception is an active process. Eye movements either alternate between fixations and saccades or follow a smooth pursuit movement in case of moving targets. Besides these macroscopic gaze patterns, the eyes perform involuntary micro-movements during fixations which are commonly categorized into micro-saccades, drift and tremor. Eye movements are frequently studied in cognitive psychology, because they reflect a complex interplay of perception, attention and oculomotor control. A common insight of psychological research is that macro-movements are highly individual. Inspired by this finding, there has been a considerable amount of prior research on oculomotoric biometric identification. However, the accuracy of known approaches is too low and the time needed for identification is too long for any practical application. This thesis explores discriminative models for the task of biometric identification. Discriminative models optimize a quality measure of the predictions and are usually superior to generative approaches in discriminative tasks. However, using discriminative models requires to select a suitable form of data representation for sequential eye gaze data; i.e., by engineering features or constructing a sequence kernel and the performance of the classification model strongly depends on the data representation. We study two fundamentally different ways of representing eye gaze within a discriminative framework. In the first part of this thesis, we explore the integration of data and psychological background knowledge in the form of generative models to construct representations. To this end, we first develop generative statistical models of gaze behavior during reading and scene viewing that account for viewer-specific distributional properties of gaze patterns. In a second step, we develop a discriminative identification model by deriving Fisher kernel functions from these and several baseline models. We find that an SVM with Fisher kernel is able to reliably identify users based on their eye gaze during reading and scene viewing. However, since the generative models are constrained to use low-frequency macro-movements, they discard a significant amount of information contained in the raw eye tracking signal at a high cost: identification requires about one minute of input recording, which makes it inapplicable for real world biometric systems. In the second part of this thesis, we study a purely data-driven modeling approach. Here, we aim at automatically discovering the individual pattern hidden in the raw eye tracking signal. To this end, we develop a deep convolutional neural network DeepEyedentification that processes yaw and pitch gaze velocities and learns a representation end-to-end. Compared to prior work, this model increases the identification accuracy by one order of magnitude and the time to identification decreases to only seconds. The DeepEyedentificationLive model further improves upon the identification performance by processing binocular input and it also detects presentation-attacks. We find that by learning a representation, the performance of oculomotoric identification and presentation-attack detection can be driven close to practical relevance for biometric applications. Eye tracking devices with high sampling frequency and precision are expensive and the applicability of eye movement as a biometric feature heavily depends on cost of recording devices. In the last part of this thesis, we therefore study the requirements on data quality by evaluating the performance of the DeepEyedentificationLive network under reduced spatial and temporal resolution. We find that the method still attains a high identification accuracy at a temporal resolution of only 250 Hz and a precision of 0.03 degrees. Reducing both does not have an additive deteriorating effect.},
  language  = {en}
}
@phdthesis{Malchow2019,
  author    = {Malchow, Martin},
  title     = {Nutzerunterst{\"u}tzung und -Motivation in E-Learning Vorlesungsarchiven und MOOCs},
  school      = {Universit{\"a}t Potsdam},
  pages     = {142},
  year      = {2019},
  abstract  = {In den letzten Jahren ist die Aufnahme und Verbreitung von Videos immer einfacher geworden. Daher sind die Relevanz und Beliebtheit zur Aufnahme von Vorlesungsvideos in den letzten Jahren stark angestiegen. Dies f{\"u}hrt zu einem großen Datenbestand an Vorlesungsvideos in den Video-Vorlesungsarchiven der Universit{\"a}ten. Durch diesen wachsenden Datenbestand wird es allerdings f{\"u}r die Studenten immer schwieriger, die relevanten Videos eines Vorlesungsarchivs aufzufinden. Zus{\"a}tzlich haben viele Lerninteressierte durch ihre allt{\"a}gliche Arbeit und famili{\"a}ren Verpflichtungen immer weniger Zeit sich mit dem Lernen zu besch{\"a}ftigen. Ein weiterer Aspekt, der das Lernen im Internet erschwert, ist, dass es durch soziale Netzwerke und anderen Online-Plattformen vielf{\"a}ltige Ablenkungsm{\"o}glichkeiten gibt. Daher ist das Ziel dieser Arbeit, M{\"o}glichkeiten aufzuzeigen, welche das E-Learning bieten kann, um Nutzer beim Lernprozess zu unterst{\"u}tzen und zu motivieren. Das Hauptkonzept zur Unterst{\"u}tzung der Studenten ist das pr{\"a}zise Auffinden von Informationen in den immer weiter wachsenden Vorlesungsvideoarchiven. Dazu werden die Vorlesungen im Voraus analysiert und die Texte der Vorlesungsfolien mit verschiedenen Methoden indexiert. Daraufhin k{\"o}nnen die Studenten mit der Suche oder dem Lecture-Butler Lerninhalte entsprechend Ihres aktuellen Wissensstandes auffinden. Die m{\"o}glichen verwendeten Technologien f{\"u}r das Auffinden wurden, sowohl technisch, als auch durch Studentenumfragen erfolgreich evaluiert. Zur Motivation von Studenten in Vorlesungsarchiven werden diverse Konzepte betrachtet und die Umsetzung evaluiert, die den Studenten interaktiv in den Lernprozess einbeziehen. Neben Vorlesungsarchiven existieren sowohl im privaten als auch im dienstlichen Weiterbildungsbereich die in den letzten Jahren immer beliebter werdenden MOOCs. Generell sind die Abschlussquoten von MOOCs allerdings mit durchschnittlich 7\% eher gering. Daher werden Motivationsl{\"o}sungen f{\"u}r MOOCs im Bereich von eingebetteten Systemen betrachtet, die in praktischen Programmierkursen Anwendung finden. Zus{\"a}tzlich wurden Kurse evaluiert, welche die Programmierung von eingebetteten Systemen behandeln. Die Verf{\"u}gbarkeit war bei Kursen von bis zu 10.000 eingeschriebenen Teilnehmern hierbei kein schwerwiegendes Problem. Die Verwendung von eingebetteten Systemen in Programmierkursen sind bei den Studenten in der praktischen Umsetzung auf sehr großes Interesse gestoßen.},
  language  = {de}
}
@phdthesis{Neuhaus2017,
  author    = {Neuhaus, Christian},
  title     = {Sicherheitsmechanismen f{\"u}r dienstbasierte Softwaresysteme},
  school      = {Universit{\"a}t Potsdam},
  pages     = {183},
  year      = {2017},
  language  = {de}
}
@phdthesis{SadrAzodi2015,
  author    = {Sadr-Azodi, Amir Shahab},
  title     = {Towards Real-time SIEM-based Network monitoring and Intrusion Detection through Advanced Event Normalization},
  school      = {Universit{\"a}t Potsdam},
  pages     = {144},
  year      = {2015},
  language  = {en}
}
@phdthesis{Saleh2016,
  author    = {Saleh, Eyad},
  title     = {Securing Multi-tenant SaaS Environments},
  school      = {Universit{\"a}t Potsdam},
  pages     = {108},
  year      = {2016},
  abstract  = {Software-as-a-Service (SaaS) offers several advantages to both service providers and users. Service providers can benefit from the reduction of Total Cost of Ownership (TCO), better scalability, and better resource utilization. On the other hand, users can use the service anywhere and anytime, and minimize upfront investment by following the pay-as-you-go model. Despite the benefits of SaaS, users still have concerns about the security and privacy of their data. Due to the nature of SaaS and the Cloud in general, the data and the computation are beyond the users' control, and hence data security becomes a vital factor in this new paradigm. Furthermore, in multi-tenant SaaS applications, the tenants become more concerned about the confidentiality of their data since several tenants are co-located onto a shared infrastructure. To address those concerns, we start protecting the data from the provisioning process by controlling how tenants are being placed in the infrastructure. We present a resource allocation algorithm designed to minimize the risk of co-resident tenants called SecPlace. It enables the SaaS provider to control the resource (i.e., database instance) allocation process while taking into account the security of tenants as a requirement. Due to the design principles of the multi-tenancy model, tenants follow some degree of sharing on both application and infrastructure levels. Thus, strong security-isolation should be present. Therefore, we develop SignedQuery, a technique that prevents one tenant from accessing others' data. We use the Signing Concept to create a signature that is used to sign the tenant's request, then the server can verifies the signature and recognizes the requesting tenant, and hence ensures that the data to be accessed is belonging to the legitimate tenant. Finally, Data confidentiality remains a critical concern due to the fact that data in the Cloud is out of users' premises, and hence beyond their control. Cryptography is increasingly proposed as a potential approach to address such a challenge. Therefore, we present SecureDB, a system designed to run SQL-based applications over an encrypted database. SecureDB captures the schema design and analyzes it to understand the internal structure of the data (i.e., relationships between the tables and their attributes). Moreover, we determine the appropriate partialhomomorphic encryption scheme for each attribute where computation is possible even when the data is encrypted. To evaluate our work, we conduct extensive experiments with di↵erent settings. The main use case in our work is a popular open source HRM application, called OrangeHRM. The results show that our multi-layered approach is practical, provides enhanced security and isolation among tenants, and have a moderate complexity in terms of processing encrypted data.},
  language  = {en}
}
@phdthesis{Schacht2014,
  author    = {Schacht, Alexander},
  title     = {Konzepte und Strategien mobiler Plattformen zur Erfassung und Anlayse von Vitalparametern in heterogenen Telemonotoring-Systemen},
  pages     = {215},
  year      = {2014},
  language  = {de}
}
@phdthesis{Schindler2016,
  author    = {Schindler, Sven},
  title     = {Honeypot Architectures for IPv6 Networks},
  school      = {Universit{\"a}t Potsdam},
  pages     = {164},
  year      = {2016},
  language  = {en}
}
@phdthesis{Schnjakin2014,
  author    = {Schnjakin, Maxim},
  title     = {Cloud-RAID},
  pages     = {137},
  year      = {2014},
  language  = {de}
}
@phdthesis{Tiwari2019,
  author    = {Tiwari, Abhishek},
  title     = {Enhancing Users' Privacy: Static Resolution of the Dynamic Properties of Android},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiii, 111},
  year      = {2019},
  abstract  = {The usage of mobile devices is rapidly growing with Android being the most prevalent mobile operating system. Thanks to the vast variety of mobile applications, users are preferring smartphones over desktops for day to day tasks like Internet surfing. Consequently, smartphones store a plenitude of sensitive data. This data together with the high values of smartphones make them an attractive target for device/data theft (thieves/malicious applications). Unfortunately, state-of-the-art anti-theft solutions do not work if they do not have an active network connection, e.g., if the SIM card was removed from the device. In the majority of these cases, device owners permanently lose their smartphone together with their personal data, which is even worse. Apart from that malevolent applications perform malicious activities to steal sensitive information from smartphones. Recent research considered static program analysis to detect dangerous data leaks. These analyses work well for data leaks due to inter-component communication, but suffer from shortcomings for inter-app communication with respect to precision, soundness, and scalability. This thesis focuses on enhancing users' privacy on Android against physical device loss/theft and (un)intentional data leaks. It presents three novel frameworks: (1) ThiefTrap, an anti-theft framework for Android, (2) IIFA, a modular inter-app intent information flow analysis of Android applications, and (3) PIAnalyzer, a precise approach for PendingIntent vulnerability analysis. ThiefTrap is based on a novel concept of an anti-theft honeypot account that protects the owner's data while preventing a thief from resetting the device. We implemented the proposed scheme and evaluated it through an empirical user study with 35 participants. In this study, the owner's data could be protected, recovered, and anti-theft functionality could be performed unnoticed from the thief in all cases. IIFA proposes a novel approach for Android's inter-component/inter-app communication (ICC/IAC) analysis. Our main contribution is the first fully automatic, sound, and precise ICC/IAC information flow analysis that is scalable for realistic apps due to modularity, avoiding combinatorial explosion: Our approach determines communicating apps using short summaries rather than inlining intent calls between components and apps, which requires simultaneously analyzing all apps installed on a device. We evaluate IIFA in terms of precision, recall, and demonstrate its scalability to a large corpus of real-world apps. IIFA reports 62 problematic ICC-/IAC-related information flows via two or more apps/components. PIAnalyzer proposes a novel approach to analyze PendingIntent related vulnerabilities. PendingIntents are a powerful and universal feature of Android for inter-component communication. We empirically evaluate PIAnalyzer on a set of 1000 randomly selected applications and find 1358 insecure usages of PendingIntents, including 70 severe vulnerabilities.},
  language  = {en}
}
@phdthesis{Wang2016,
  author    = {Wang, Cheng},
  title     = {Deep Learning of Multimodal Representations},
  school      = {Universit{\"a}t Potsdam},
  pages     = {142},
  year      = {2016},
  language  = {en}
}
@phdthesis{Weber2015,
  author    = {Weber, Edzard},
  title     = {Erarbeitung einer Methodik der Wandlungsf{\"a}higkeit},
  school      = {Universit{\"a}t Potsdam},
  pages     = {471},
  year      = {2015},
  language  = {de}
}
@phdthesis{Wust2015,
  author    = {Wust, Johannes},
  title     = {Mixed workload managment for in-memory databases},
  pages     = {VIII, 167},
  year      = {2015},
  language  = {en}
}