Privacy engineering and particularly privacy threat modelling have gained a lot of attention in the recent years. Many methodologies have been proposed to model privacy threats. An example of such methods is the widely used LINDDUN method. As some recent (ISO/IEC) standards and regulations (e.g., GDPR) require handling risks associated with the elicited threats, we combined the LINDDUN method with a privacy risk rating method forming a holistic method that takes the system model as input and outputs a list of privacy risks. Your task in this project/thesis work is to implement a tool to support the the deployment of our developed method. Related work to such a tool is the commonly used Microsoft threat modelling tool which is used for security. Another very related example that is considered an extension to the MS tool is the TMTe4PT tool. There are no restrictions on the technologies or languages used in the implementation as long as it achieves the required features similarly to, e.g., TMTe4PT. This project can also be extended to a thesis by including research questions related to the countermeasure selection process.

Zero Trust Security is currently one of the most rising network security concepts. The concept was originally proposed to solve the flaws of the still predominant Perimeter Security. Preventing network internal attacker’s lateral movement is one of the core goals of Zero Trust Security. This goal is supposed to achieved i.a. by strictly enforced authentication, authorization, and least privilege approaches. One of the core components to perform these tasks are Policy Enforcement Points (PEP) in combination with Policy Decision Points (PDP). Each request asking for permission to access an network internal resource must be authenticated at the PEP before it is forwarded to the actual resource. In addition, coarse-grained authorization decisions can be enforced here. The actual decision is forwarded to the PDP that uses statically or dynamically defined authentication as well as authorization policies. The PEP is informed about the decision and must enforce it. Examples for existing open-source PEP/PDP solutions are – Pritunl Zero (https://github.com/pritunl/pritunl-zero) – Pomerium (https://github.com/pomerium/pomerium) – ORY Oathkeeper (https://github.com/ory/oathkeeper) The goal of this thesis is to comprehensively evaluate existing PEP/PDP solutions against some predefined criteria as the security state or the performance of the solutions. For master students it is expected to also expand the most promising solution by features according to some predefined use cases.

PaySim, a Mobile Money Payment Simulator simulates money transactions between users based on Multi Agent Based Simulation (MABS). It also generates data that can be used to test algorithms which should detect suspicious activities or fraud. This generated data is based on real financial data, which cannot be published for security reasons. In order to use or train the detection algorithms on real data, the synthetic information should be as similar as possible to the real one but not exactly the same. In this project or thesis, you should read the work of A. Elmir and E. Lopez-Rojas (PaySim), as well as the theory of MABS. Then you should implement a similar program to PaySim, which has certain data as input and should output generated synthetic data which fulfills the above requirement. As a test, you have to use the VeReMi Dataset where detection algorithms and results already exist. Then, the tool will be used on CAN messages.

Angular is a web framework for single-page application, i.e., most business logic resides in the browser not on the server. The server is contact by a REST interface, mainly used to get direct access to the application data. Shibboleth is an authentication technology used also by KIZ to authenticate and authorise web access. In this work, a simple demo application has to be developed together with a concept for authenticating users and authorisation of their application-logic and REST-based data accesses. Ideally the concept is some sort of library including guidelines, and is tested against the KIZ identity provider. This work includes some basic user management in the application to recognise already known users and attach preferences etc. to it. Challenges are user-authentication expiry during user sessions and version updates in the backend server during the life time of the single-page application.

Security assessments of networked systems require knowledge about the utilized communication protocol. For proprietary protocols without known specification and with only limited access to the end-points, the only source of information is the communication itself. To correctly conclude from the captured byte stream to message-formats, -types, and finally a protocol model, structure, message- and field-boundaries, data-type, and semantics need to be inferred.After an initial inference procedure, it is desirable to refine the existing protocol model. Additional information gained by recorded network traffic needs to be incorporated by recognizing the appropriate parts of the model. The modeled knowledge is to be extended depending on the applicable information inferable from the new trace.

To build powerful machine learning models, lots of data is required. However, obtaining the data comes with privacy risks for the people or entities that provide their data. Recently, Google published TensorFlow Federated - an open source framework to allow machine learning on decentralized data. The approach of federated learning makes machine learning in the age of mobile devices and wearables both more efficient, as well as more privacy-friendly. The goal of this thesis or project is to become familiar with the TensorFlow Federated framework, to understand and be able to explain the techniques which are implemented in it, to be able to build machine learning models in a federated way, and possibly to implement own enhancements of the framework.

Machine learning offers great opportunities, but also comes with risks. Especially the privacy risks are becoming more prevalent in the discussions about machine learning. Recently, Google published a machine learning library called TensorFlow Privacy. Its goal is to make it easier for developers and researchers to build privacy-preserving machine learning models. Specifically, it utilizes Differential Privacy, which mathematically guarantees that the training data to create the models is protected from being extracted. The goal of this thesis or project is to become familiar with the TensorFlow Privacy library, to understand and be able to explain the techniques which are implemented in it, to be able to build privacy-preserved machine learning models, and possibly to implement own protection techniques that could enhance the TensorFlow Privacy library.

Security assessments of networked systems require knowledge about the utilized communication protocol. For proprietary protocols without known specification and with only limited access to the end-points, the only source of information is the communication itself. To correctly conclude from the captured byte stream to message-formats, -types, and finally a protocol model, structure, message- and field-boundaries, data-type, and semantics need to be inferred.After an initial inference procedure, it is desirable to refine the existing protocol model. Actively probing an entity for the validity of message syntaxes allows to targetedly enhance the knowledge about the protocol. To do this efficiently a smart method of automatically generating test-cases depending on the current protocol model needs to be developed.

Modern vehicles will use communication to increase the safety of its passengers, reduce fuel consumption, travel time, and more. The communication between the vehicles will be mainly beacon messages containing the speed, position, acceleration and other properties. These messages need to be validated, if they contain correct (plausible) information. For example, when a vehicle is suddenly stopping, but sending an increase in speed, the following vehicles may crash into the misbehaving vehicle. In literature, there is already existing work on detecting misbehavior in the data with different techniques such as subjective logic or machine learning. In this project, we will analyze the VeReMi data-set with the help of different machine learning algorithms. The number of algorithms compared is depending on the scope (credits). The student can choose the framework, e.g. PyTorch.

Encryption is one of the most reliable techniques for protecting information. However, once data is encrypted, using it becomes very difficult. Goal of this thesis or project, is to explore how Machine Learning algorithms can be designed to be able to deal with encrypted data. Firstly, a survey of existing mechanisms should be conducted. In a second part, algorithms will be comparatively implemented, or own encryption mechanisms introduced.

Privacy engineering and particularly privacy threat modelling have gained a lot of attention in the recent years. Many methodologies have been proposed to model privacy threats. An example of such methods is the widely used LINDDUN method. As some recent (ISO/IEC) standards and regulations (e.g., GDPR) require handling risks associated with the elicited threats, we combined the LINDDUN method with a privacy risk rating method forming a holistic method that takes the system model as input and outputs a list of privacy risks. Your task in this project/thesis work is to implement a tool to support the the deployment of our developed method. Related work to such a tool is the commonly used Microsoft threat modelling tool which is used for security. Another very related example that is considered an extension to the MS tool is the TMTe4PT tool. There are no restrictions on the technologies or languages used in the implementation as long as it achieves the required features similarly to, e.g., TMTe4PT. This project can also be extended to a thesis by including research questions related to the countermeasure selection process.

Zero Trust Security is currently one of the most rising network security concepts. The concept was originally proposed to solve the flaws of the still predominant Perimeter Security. Preventing network internal attacker’s lateral movement is one of the core goals of Zero Trust Security. This goal is supposed to achieved i.a. by strictly enforced authentication, authorization, and least privilege approaches. One of the core components to perform these tasks are Policy Enforcement Points (PEP) in combination with Policy Decision Points (PDP). Each request asking for permission to access an network internal resource must be authenticated at the PEP before it is forwarded to the actual resource. In addition, coarse-grained authorization decisions can be enforced here. The actual decision is forwarded to the PDP that uses statically or dynamically defined authentication as well as authorization policies. The PEP is informed about the decision and must enforce it. Examples for existing open-source PEP/PDP solutions are – Pritunl Zero (https://github.com/pritunl/pritunl-zero) – Pomerium (https://github.com/pomerium/pomerium) – ORY Oathkeeper (https://github.com/ory/oathkeeper) The goal of this thesis is to comprehensively evaluate existing PEP/PDP solutions against some predefined criteria as the security state or the performance of the solutions. For master students it is expected to also expand the most promising solution by features according to some predefined use cases.

Security assessments of networked systems require knowledge about the utilized communication protocol. For proprietary protocols without known specification and with only limited access to the end-points, the only source of information is the communication itself. To correctly conclude from the captured byte stream to message-formats, -types, and finally a protocol model, structure, message- and field-boundaries, data-type, and semantics need to be inferred.After an initial inference procedure, it is desirable to refine the existing protocol model. Additional information gained by recorded network traffic needs to be incorporated by recognizing the appropriate parts of the model. The modeled knowledge is to be extended depending on the applicable information inferable from the new trace.

To build powerful machine learning models, lots of data is required. However, obtaining the data comes with privacy risks for the people or entities that provide their data. Recently, Google published TensorFlow Federated - an open source framework to allow machine learning on decentralized data. The approach of federated learning makes machine learning in the age of mobile devices and wearables both more efficient, as well as more privacy-friendly. The goal of this thesis or project is to become familiar with the TensorFlow Federated framework, to understand and be able to explain the techniques which are implemented in it, to be able to build machine learning models in a federated way, and possibly to implement own enhancements of the framework.

Machine learning offers great opportunities, but also comes with risks. Especially the privacy risks are becoming more prevalent in the discussions about machine learning. Recently, Google published a machine learning library called TensorFlow Privacy. Its goal is to make it easier for developers and researchers to build privacy-preserving machine learning models. Specifically, it utilizes Differential Privacy, which mathematically guarantees that the training data to create the models is protected from being extracted. The goal of this thesis or project is to become familiar with the TensorFlow Privacy library, to understand and be able to explain the techniques which are implemented in it, to be able to build privacy-preserved machine learning models, and possibly to implement own protection techniques that could enhance the TensorFlow Privacy library.

Security assessments of networked systems require knowledge about the utilized communication protocol. For proprietary protocols without known specification and with only limited access to the end-points, the only source of information is the communication itself. To correctly conclude from the captured byte stream to message-formats, -types, and finally a protocol model, structure, message- and field-boundaries, data-type, and semantics need to be inferred.After an initial inference procedure, it is desirable to refine the existing protocol model. Actively probing an entity for the validity of message syntaxes allows to targetedly enhance the knowledge about the protocol. To do this efficiently a smart method of automatically generating test-cases depending on the current protocol model needs to be developed.

Modern vehicles will use communication to increase the safety of its passengers, reduce fuel consumption, travel time, and more. The communication between the vehicles will be mainly beacon messages containing the speed, position, acceleration and other properties. These messages need to be validated, if they contain correct (plausible) information. For example, when a vehicle is suddenly stopping, but sending an increase in speed, the following vehicles may crash into the misbehaving vehicle. In literature, there is already existing work on detecting misbehavior in the data with different techniques such as subjective logic or machine learning. In this project, we will analyze the VeReMi data-set with the help of different machine learning algorithms. The number of algorithms compared is depending on the scope (credits). The student can choose the framework, e.g. PyTorch.

Neurofeedback provides the necessary means to visualize selected and controlled parameters of the brain activity. In healthcare domain, neurofeedback studies enable mitigation of many psychological disorders and illnesses, mainly by therapies that help patients to better self-regulate their brain activity. Electroencephalography (EEG) is the method of monitoring the electrical activity of the brain, thus providing the necessary feedback. In this thesis work, the student is required to survey the current state of frameworks, techniques, or methods that enable coupling of Mobile EEGs with Smartphones. Bluetooth 2.1 with Enhanced Data Rate (EDR) capability is one of the most effective mean of coupling EEGs with Smartphones. The student would therefore be required to work on the Bluetooth stack to acquire real-time data generated from the Mobile EEGs, parse the electrical signal, and visualize the signal semantically. For successful completion of the thesis, the student would be required to identify and address any one of the open challenges faced by the proposed topic. An example of this can be addressing the bandwidth challenges, battery consumption, or signal accuracy

Encryption is one of the most reliable techniques for protecting information. However, once data is encrypted, using it becomes very difficult. Goal of this thesis or project, is to explore how Machine Learning algorithms can be designed to be able to deal with encrypted data. Firstly, a survey of existing mechanisms should be conducted. In a second part, algorithms will be comparatively implemented, or own encryption mechanisms introduced.

Zero Trust Security is currently one of the most rising network security concepts. The concept was originally proposed to solve the flaws of the still predominant Perimeter Security. Preventing network internal attacker’s lateral movement is one of the core goals of Zero Trust Security. This goal is supposed to achieved i.a. by strictly enforced authentication, authorization, and least privilege approaches. One of the core components to perform these tasks are Policy Enforcement Points (PEP) in combination with Policy Decision Points (PDP). Each request asking for permission to access an network internal resource must be authenticated at the PEP before it is forwarded to the actual resource. In addition, coarse-grained authorization decisions can be enforced here. The actual decision is forwarded to the PDP that uses statically or dynamically defined authentication as well as authorization policies. The PEP is informed about the decision and must enforce it. Examples for existing open-source PEP/PDP solutions are – Pritunl Zero (https://github.com/pritunl/pritunl-zero) – Pomerium (https://github.com/pomerium/pomerium) – ORY Oathkeeper (https://github.com/ory/oathkeeper) The goal of this thesis is to comprehensively evaluate existing PEP/PDP solutions against some predefined criteria as the security state or the performance of the solutions. For master students it is expected to also expand the most promising solution by features according to some predefined use cases.

Security assessments of networked systems require knowledge about the utilized communication protocol. For proprietary protocols without known specification and with only limited access to the end-points, the only source of information is the communication itself. To correctly conclude from the captured byte stream to message-formats, -types, and finally a protocol model, structure, message- and field-boundaries, data-type, and semantics need to be inferred.After an initial inference procedure, it is desirable to refine the existing protocol model. Additional information gained by recorded network traffic needs to be incorporated by recognizing the appropriate parts of the model. The modeled knowledge is to be extended depending on the applicable information inferable from the new trace.

Security assessments of networked systems require knowledge about the utilized communication protocol. For proprietary protocols without known specification and with only limited access to the end-points, the only source of information is the communication itself. To correctly conclude from the captured byte stream to message-formats, -types, and finally a protocol model, structure, message- and field-boundaries, data-type, and semantics need to be inferred.After an initial inference procedure, it is desirable to refine the existing protocol model. Actively probing an entity for the validity of message syntaxes allows to targetedly enhance the knowledge about the protocol. To do this efficiently a smart method of automatically generating test-cases depending on the current protocol model needs to be developed.

Neurofeedback provides the necessary means to visualize selected and controlled parameters of the brain activity. In healthcare domain, neurofeedback studies enable mitigation of many psychological disorders and illnesses, mainly by therapies that help patients to better self-regulate their brain activity. Electroencephalography (EEG) is the method of monitoring the electrical activity of the brain, thus providing the necessary feedback. In this thesis work, the student is required to survey the current state of frameworks, techniques, or methods that enable coupling of Mobile EEGs with Smartphones. Bluetooth 2.1 with Enhanced Data Rate (EDR) capability is one of the most effective mean of coupling EEGs with Smartphones. The student would therefore be required to work on the Bluetooth stack to acquire real-time data generated from the Mobile EEGs, parse the electrical signal, and visualize the signal semantically. For successful completion of the thesis, the student would be required to identify and address any one of the open challenges faced by the proposed topic. An example of this can be addressing the bandwidth challenges, battery consumption, or signal accuracy

Angular is a web framework for single-page application, i.e., most business logic resides in the browser not on the server. The server is contact by a REST interface, mainly used to get direct access to the application data. Shibboleth is an authentication technology used also by KIZ to authenticate and authorise web access. In this work, a simple demo application has to be developed together with a concept for authenticating users and authorisation of their application-logic and REST-based data accesses. Ideally the concept is some sort of library including guidelines, and is tested against the KIZ identity provider. This work includes some basic user management in the application to recognise already known users and attach preferences etc. to it. Challenges are user-authentication expiry during user sessions and version updates in the backend server during the life time of the single-page application.

Privacy engineering and particularly privacy threat modelling have gained a lot of attention in the recent years. Many methodologies have been proposed to model privacy threats. An example of such methods is the widely used LINDDUN method. As some recent (ISO/IEC) standards and regulations (e.g., GDPR) require handling risks associated with the elicited threats, we combined the LINDDUN method with a privacy risk rating method forming a holistic method that takes the system model as input and outputs a list of privacy risks. Your task in this project/thesis work is to implement a tool to support the the deployment of our developed method. Related work to such a tool is the commonly used Microsoft threat modelling tool which is used for security. Another very related example that is considered an extension to the MS tool is the TMTe4PT tool. There are no restrictions on the technologies or languages used in the implementation as long as it achieves the required features similarly to, e.g., TMTe4PT. This project can also be extended to a thesis by including research questions related to the countermeasure selection process.

Zero Trust Security is currently one of the most rising network security concepts. The concept was originally proposed to solve the flaws of the still predominant Perimeter Security. Preventing network internal attacker’s lateral movement is one of the core goals of Zero Trust Security. This goal is supposed to achieved i.a. by strictly enforced authentication, authorization, and least privilege approaches. One of the core components to perform these tasks are Policy Enforcement Points (PEP) in combination with Policy Decision Points (PDP). Each request asking for permission to access an network internal resource must be authenticated at the PEP before it is forwarded to the actual resource. In addition, coarse-grained authorization decisions can be enforced here. The actual decision is forwarded to the PDP that uses statically or dynamically defined authentication as well as authorization policies. The PEP is informed about the decision and must enforce it. Examples for existing open-source PEP/PDP solutions are – Pritunl Zero (https://github.com/pritunl/pritunl-zero) – Pomerium (https://github.com/pomerium/pomerium) – ORY Oathkeeper (https://github.com/ory/oathkeeper) The goal of this thesis is to comprehensively evaluate existing PEP/PDP solutions against some predefined criteria as the security state or the performance of the solutions. For master students it is expected to also expand the most promising solution by features according to some predefined use cases.

Since Google introduced their BeyondCorp project, Zero Trust (ZT) is one of the most popular buzzwords in the area of network security. In a ZT network, Policy Enforcement Point (PEP) and Policy Decision Point (PDP) are responsible for central authentication and authorization (Auth*). Both mentioned components and conventional security functions such as firewalls work largely independently of each other when it comes to processing packets. This leads to inefficient scenarios in which all packets are processed by time- consuming security functions. By coupling the conventional security functions to the PEP/PDP, higher efficiency in security-relevant packet processing can be achieved. This can be achieved by leveraging the Service Function Chaining (SFC) approach. SFC allows the dynamic chaining of conventional network service functions such as HTTP header enricher or firewalls. For each network flow can be decided what service function should be applied to all the flow's packets. The PEP/PDP in a ZT network acts then as the orchestrator, decides about the functions that should be chained together. By doing this, it can be efficiently decided which function should be applied. The goal of the project is to implement one of the thus orchestrated security service functions namely a Multi Factor Authenticator (MFA) that is embedded in a already existing Zero Trust SFC prototype. The MFA must be HTTP based and written in Go. Requirements: Good knowledge of Go and security protocols).

As part of our ongoing research, are we currently building a platform for secure statistical analysis based on SGX. The current prototype relies on a very simple statistics language, which we are planning to extend in the future. The goal of this project is to port an existing statistics language interpreter, such as PSPP, to the Rust programming language. Rust features a rich type system and can guarantee memory-safety and thread-safety during compile time, which makes it a great candidate for building safe and fast programming language interpreters. nom is a parser combinators library written in Rust that allows to build safe parsers without compromising on speed or memory consumption. This library can be used as a starting point to implement the parser.

Encryption is one of the most reliable techniques for protecting information. However, once data is encrypted, using it becomes very difficult. Goal of this thesis or project, is to explore how Machine Learning algorithms can be designed to be able to deal with encrypted data. Firstly, a survey of existing mechanisms should be conducted. In a second part, algorithms will be comparatively implemented, or own encryption mechanisms introduced.

Privacy engineering and particularly privacy threat modelling have gained a lot of attention in the recent years. Many methodologies have been proposed to model privacy threats. An example of such methods is the widely used LINDDUN method. As some recent (ISO/IEC) standards and regulations (e.g., GDPR) require handling risks associated with the elicited threats, we combined the LINDDUN method with a privacy risk rating method forming a holistic method that takes the system model as input and outputs a list of privacy risks. Your task in this project/thesis work is to implement a tool to support the the deployment of our developed method. Related work to such a tool is the commonly used Microsoft threat modelling tool which is used for security. Another very related example that is considered an extension to the MS tool is the TMTe4PT tool. There are no restrictions on the technologies or languages used in the implementation as long as it achieves the required features similarly to, e.g., TMTe4PT. This project can also be extended to a thesis by including research questions related to the countermeasure selection process.

Neurofeedback provides the necessary means to visualize selected and controlled parameters of the brain activity. In healthcare domain, neurofeedback studies enable mitigation of many psychological disorders and illnesses, mainly by therapies that help patients to better self-regulate their brain activity. Electroencephalography (EEG) is the method of monitoring the electrical activity of the brain, thus providing the necessary feedback. In this thesis work, the student is required to survey the current state of frameworks, techniques, or methods that enable coupling of Mobile EEGs with Smartphones. Bluetooth 2.1 with Enhanced Data Rate (EDR) capability is one of the most effective mean of coupling EEGs with Smartphones. The student would therefore be required to work on the Bluetooth stack to acquire real-time data generated from the Mobile EEGs, parse the electrical signal, and visualize the signal semantically. For successful completion of the thesis, the student would be required to identify and address any one of the open challenges faced by the proposed topic. An example of this can be addressing the bandwidth challenges, battery consumption, or signal accuracy