Event-sourced graphs are a concept to capture graphs that are continuously evolving, e.g. based on a stream of live changes. Goal of this thesis is (a) to provide an overview of existing graph machine learning concepts, (b) to assess the applicability of machine learning mechansims on event-sourced graphs, and (c) to prove the feasibility with a prototype implementation. Basic knowledge of general machine learning concepts is a plus for this thesis.

Wireless Sensor Networks consist of sensor nodes that utilize low-power wide area networks to periodically transmit sensor readings (e.g., temperature, humidity, pollution levels) to backend services. These sensor nodes are typically battery-powered and their processing capabilities hence limited. This directly results in a trade-off between temporal resolution of transmitted sensor data and battery lifespan. As part of a research project at the Institute of Distributed System, we research novel storage mechanisms at the sensor node to reduce the temporal resolution by default, but still allowing for high-resolution on-demand access of past readings. The goal of this topic is to investigate the power consumption characteristics of LoRa sensor devices in regard to storing sensor readings in non-volatile memory on the sensor node and transmitting data to the network. This problem can be investigated through the means of network simulations (e.g., using the FLoRa framework for OMNeT++ or similar tools) and analytical calculations. Additionally, practical experiments on real LoRa hardware can be performed to validate the analytical results (not absolutely required for the topic).

Event-sourced graph computing allows to run computations on the latest state or on historical states of an evolving graph. Such event-sourced graphs capture highly connected application domains that are continuously evolving, e.g. based on streams of live changes. An advanced use case is the application of event-sourced graph computing with mutitple concurrent parties that feed in changes into the system and also run computations. Multi tenancy however requires novel mechanisms to address private computations and data access control. Goal of this thesis is (a) to compile a list of challenges when applying multi-tenancy graph computing, (b) to survey and assess possible security solutions, and (c) to contribute a prototype implementation. Basic knowledge of distributed systems and IT security is recommended for this thesis.

We have a collection of Java programming tasks as part of our introduction to computer networks course. In these programming tasks students are instructed to implement network applications matching a strict set of rules. The goal of this project is to implement a rule-based testing framework to aid the grading of such programming tasks and providing immediate feedback to students whether their implementation is correct or not.

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.