Evaluation of Modern Trusted Execution Environments

Tim Ohlendorf, available immediately

The concept of Trusted Execution Environments (TEE) allows secure, isolated execution of trusted applications on various types of devices. In detail, the implementations of TEEs on different platforms like ARM TrustZone, Intel SGX, etc. vary in provided functionality and level of security.

In this thesis, TEE implementations should be investigated and evaluated with regard to provided functionality, security and privacy.

Requirements

Basic knowledge about IT security. Ideally, already gained initial experience in the area of Trusted Computing & mobile / embedded systems.

Mobile Vulnerability Management

Tim Ohlendorf, available immediately

In today’s world, the smartphone is used as a daily driver for a variety of digital services. As the common mobile platforms Android and iOS offer more and more sophisticated security and privacy mechanisms, the smartphone can also be used for sensitive applications in the context of digital identities (eIDAS eID, electronic health card, digital travel credentials and more). However, the use of smartphones as a medium for digital identities is often highly regulated, especially in the eGovernment and eHealth sector. Beside being security certified (e.g. Common Criteria),  smartphones holding regulated digital identities have to be monitored for known vulnerabilities and exploits.
In this thesis, an approach which monitors common vulnerability and exploit databases (e.g. Exploit DB) and automatically assigns applicable vulnerabilities to affected smartphone models should be investigated and implemented.

Requirements

Basic knowledge about IT security. Ideally, already gained initial experience in the area of Android security & vulnerability management.

Evaluation of eIDAS Compliant Mobile eID Schemes in the European Union

Tim Ohlendorf, available immediately

In the digital world electronic identification (eID) plays an increasingly important role, slowly replacing physical identity documents in both eGovernment and commercial domains. With eIDAS (electronic IDentification, Authentication and trust Services), the European Union introduced a regulation which defines common, abstract standards and rules for eID and eSignature solutions in the European internal market. In result, this enables a holder of an eIDAS compliant eID means to identify and authenticate towards public services all across the European Union. Therefore, several European countries and commercial entities have chosen different security mechanisms varying from software-based up to hardware-based, often claiming the same level of trustworthiness. In this thesis, current mobile eID schemes in the European Single Market should be investigated regarding the chosen eID technologies, as well as the implemented security and privacy mechanisms.

Requirements

Basic knowledge about (mobile) IT security. Ideally, already gained initial experience in the area of mobile eID solutions and eIDAS.

Future Attacks And Malware For the IoT

Tim Ohlendorf, available immediately

The number of Internet of Things (IoT) devices is rising. Due to missing knowledge and commercial interests, most of these devices lack of a good security design. Therefore malware attacks are an emerging thread and their impact is endless due to the various usage scenarios of the IoT devices (smart home, medical sector, industrial automation systems, automobile systems, ...). This thesis should investigate possible future attacks on IoT devices and how they could be used by different kind of attackers (governmental attacker, attacker with commercial interests, ...). Finally, the design and demonstration of an advanced malware attack against a testbed running a typical IoT environment (e.g. smart home, ...) should be carried out.

Requirements

Basic knowledge about IT security. Knowledge in embedded systems development. Ideally, already gained initial experience in the area of exploiting IoT devices and IoT malware.

Choosing a Privacy Parameter for Differentially Private Machine Learning

Franziska Boenisch, Maija Poikela, available immediately

Differential Privacy (DP) is a mathematical framework that provides privacy guarantees to individual data points of a dataset when doing analyzes on that data. In DP, the amount of privacy is usually expressed by a privacy parameter called epsilon. A small value of epsilon corresponds to high privacy, whereas a large value of epsilon offers only weak privacy guarantees. However, the exact value of epsilon is always dependent on the dataset and on the ML model that is trained with that data. Therefore, this thesis investigates the dependence between epsilon, the ML model and the privacy level of data in depth. Based on the findings, it develops models for determinating an adequate value epsilon given a set of requirements. The resulting models should also be capable of combining data points that require different levels of privacy, and hence different values for epsilon.

Attacking Differentially Private Machine Learning Models

Franziska Boenisch, Maija Poikela, available immediately

Differential Privacy (DP) is a mathematical framework that provides privacy guarantees to individual data points of a dataset when doing analyzes on that data. In DP, the amount of privacy is usually expressed by a privacy parameter called epsilon. When choosing an inadequate value for epsilon, the resulting ML model may leak private information about the potentially sensitive training data. This thesis is intended to develop attacks against DP ML models. Such attacks may target the disclosure of specific data points’ or the entire dataset’s attributes, reverse-engineering of the model (model inversion), or the disclosure of (non-)membership of specific data points (membership inference). The developed attacks are implemented and used to evaluate existing DP ML models.

Studying Security Measures in Machine Learning Applications on GitHub

Franziska Boenisch, Maija Poikela, available immediately

In recent years, the number of machine learning applications has grown exponentially. However, due to limited resources (time, personnel, know-how), not a lot of focus has been given to securing those applications against the growing number of threats (data poisoning, adversarial learning, model exploration, dependencies of many libraries). 

The focus of this thesis is to depict the current state of the art in securing machine learning. The first part will be theoretical in form of a literature survey. It should consider the current literature summarizing the threats to machine learning, the attacks that are currently known, and the possible defenses. The second part will be practical in form from studying relevant machine learning code examples on GitHub qualitatively. The first task is to identify a number of relevant repositories (that provide machine learning applications and that have a user-base rather than being, for example, a student’s personal homework repository). The second task is to qualitatively analyze the security of the machine learning models by studying the measures taken for security, the dependencies of the libraries used, etc. The third task is to quantitatively study the security measures for machine learning on GitHub based on the large numbers of repositories that you have gone through. Initiating from that point, you can extend the scope of the thesis to several directions: perform user studies with developers to study why or why not are they applying any or certain measures, develop a new or implement an existing theoretical attack (and apply it to a specific application on GitHub), study security and vulnerability aspects of a library that you have found to appear commonly in machine learning.

In case of a good quality outcome, a scientific publication of the results is a possible prospect.

Please provide your CV and a short motivation letter.

Requirements

Ability to work independently and scientifically, deep understanding of machine learning, experience in working with GitHub, profound knowledge of at least one high-level programming language, interest in security and threat models. Also fluency in English, as well as good communication skills are a must.

Untersuchung und Implementierung von Verfahren zur sicheren Darstellung von Inhalten auf dem Bildschirm von Smartphones.

Marian Margraf, ab sofort

Immer häufiger verwenden Benutzer Apps, mit denen sich Kundenkarten und Mitgliedskarten auf mobilen Endgeräten speichern und auf dem Bildschirm z.B. als Barcode darstellen lassen. Visuelle Inhalte können aber beispielsweise durch Overlays manipuliert werden. Möchte man ein Smartphone als Ausweis auch offline nutzen könnte, müssten die auf dem Bildschirm dargestellten Inhalte gegen Manipulations- und Fälschungsversuche geschützt werden. Im Rahmen dieser Abschlussarbeit soll der aktuelle Stand der Wissenschaft und Technik, die Patentlage sowie derzeitige wie zukünftige technische Mittel zu Absicherung des Display-Inhalts (z.B. TEE) untersucht werden.

Voraussetzungen

• Einarbeitung in hardwarebasierte Sicherheitsanker auf mobilen Endgeräten (TEE, SE)
• gute Kenntnisse in der Entwicklung mobiler Anwendungen
• Kenntnisse in kryptographischen Verfahren

Implementierung und Evaluierung der im Rahmen der Studie „Online-Ausweisfunktion auf dem Smartphone“ vorgestellten Umsetzung hardwarebasierter Schutzmaßnahmen für personenbezogene Daten auf dem iPhone.

Marian Margraf, ab sofort

Im Rahmen der Studie „Online Ausweisfunktion auf dem Smartphone“ wurde eine Reihe von hardwarebasierten Schutzmaßnahmen für personenbezogene Daten auf dem iPhone vorgestellt. Hierzu gehört u.a. die sichere Umsetzung einer Zwei-Faktor-Authentifizierung. Im Rahmen der Abschlussarbeit soll dieses Verfahren auf dem iPhone umgesetzt und die Ergebnisse evaluiert werden. Hierbei soll der gesamte Lebenszyklus – Registrierung, Verwendung, Sperrung, Wieder- aufnahme und Löschung – des Schlüsselmaterials sowie der damit verbundenen Identität betrachtet werden.

Voraussetzungen

• Einarbeitung in den Themenkomplex e-ID
• Einarbeitung in Sicherheitsrichtlinien des BSI
• Einarbeitung in die technischen Richtlinien der eIDAS Verordnung
• gute Kenntnisse in der Entwicklung mobiler Anwendungen
• Kenntnisse in kryptographischen Verfahren

Implementierung und Evaluierung der im Rahmen der Studie „Online-Ausweisfunktion auf dem Smartphone“ vorgestellten Umsetzung hardwarebasierter Schutzmaßnahmen für personenbezogene Daten auf einem Android-Smartphone.

Marian Margraf, ab sofort

Im Rahmen der Studie „Online Ausweisfunktion auf dem Smartphone“ wurde eine Reihe von hardwarebasierten Schutzmaßnahmen für personenbezogene Daten auf Android-Smartphones vorgestellt. Hierzu gehört u.a. die sichere Umsetzung einer Zwei-Faktor-Authentifizierung. Im Rahmen der Abschlussarbeit soll dieses Verfahren auf einem Android-Smartphone umgesetzt und die Ergebnisse evaluiert werden. Hierbei soll der gesamte Lebenszyklus – Registrierung, Verwendung, Sperrung, Wiederaufnahme und Löschung – des Schlüsselmaterials sowie der damit verbundenen Identität betrachtet werden.

Voraussetzungen

• Einarbeitung in den Themenkomplex e-ID
• Einarbeitung in Sicherheitsrichtlinien des BSI
• Einarbeitung in die technischen Richtlinien der eIDAS Verordnung
• gute Kenntnisse in der Entwicklung mobiler Anwendungen
• Kenntnisse in kryptographischen Verfahre