Controlling a Robotic Arm with 5G, BACHELOR/MASTER Thesis
Industrial facilities use 5G campus networks to control their production units and exchange information. Compared to WiFi setups, such 5G campus networks benefit from the security and performance capabilities of the latest mobile generation. However, we know a series of attacks that enable an adversary to interfere with components in a 5G network setup, and campus networks are not necessarily excluded from this threat.
In this thesis, you build a demonstrator setup that involves a robotic arm and a 5G network component. To this end, you will 3D-print a suitable model, assemble it, and connect it to a 5G lab network under your control. After the assembly, you will setup a toy example that allows you to control the robotic arm through the 5G cellular connection. In the final setup, you conduct a security analysis of the setup and reflect on the individual requirements in an industrial 5G campus network.
Mobile Communication
Location Verification in Car2X Communication, MASTER Thesis
Cars exchange information with other vehicles, roadside units, or pedestrians and incorporate this data in, e.g., the navigation system. 5G enables such communication in a decentralized way where participants exchange information directly, meaning that no network infrastructure is needed. The correctness of exchanged location information is crucial for reasonable decision-making and a manipulation can affect the safety of participants in the traffic.
In this thesis, you implement a simulation model that allows for imitating the exchange of information between mobile participants in traffic. As part of the evaluation, you implement a verification mechanism that validates the location information of vehicles.
Privacy
Biased Overlay Networks, BACHELOR/MASTER Thesis An overlay network provides a service on
the application layer and uses the existing infrastructure of the Internet. One example of this is Tor, a system
that provides additional security and privacy for online connections. The level of protection that such a
connection can offer, however, depends on the infrastructure of the underlying network. For example, if the
traffic is routed through countries that monitor and censor Tor, transmissions are much more likely to be
targeted by attacks. In this thesis, your analyze the bias that the Internet infrastructure introduces for
overlay networks. To this end, you use empirical information of the Internet and match it with the
infrastructure of overlay networks. After implementing a simulation model, you analyze how the Internet
influences the security and privacy of connections and how it opens up attack vectors.
AI-Driven Security Enhancements in 6G Network Architectures
AI-based Network Planning for Secure 6G Campus Networks, MASTER Thesis Context & Motivation: Network planning strongly impacts both performance and security of mobile networks. Poor placement of base stations may enable attacks such as IMSI-Catchers or rogue base stations. AI-based optimization can help design topologies that minimize risks while maintaining performance. Scientific Relevance: Integrating security considerations directly into network planning is novel in 6G research. Existing approaches rarely combine topology optimization with security metrics. Candidate Tasks: Conduct literature study on network planning and security metrics; build a simulation model of campus networks; design and train an ML algorithm for secure cell placement; evaluate the approach against realistic attack and usage scenarios. Tools / Skills: ns-3, Python (TensorFlow/PyTorch), cellular networks knowledge (5G/6G), machine learning
Deep Learning for Anomaly Detection in the 6G Core, MASTER Thesis Context & Motivation: The 6G core network is a critical target for attacks such as denial-of-service or rogue functions. Classical intrusion detection systems struggle with scalability. Deep learning-based anomaly detection promises more robust protection. Scientific Relevance: While anomaly detection is well-studied, its application to 6G core functions is largely unexplored. Candidate Tasks: Analyze 6G core functions and attack vectors; collect or generate relevant network traffic data; implement and train DL-based anomaly detection models (Autoencoder, RNN, CNN); benchmark performance against classical IDS methods. Tools / Skills: Python, TensorFlow/Keras, Mininet/Open5GS, network security
Simulation and Analysis of Attack Scenarios in 6G Network Structures, MASTER Thesis Context & Motivation: 6G networks will be highly complex, making realistic attack simulations (IMSI-Catcher, rogue base stations, traffic injection) essential for evaluating countermeasures. Scientific Relevance: Systematic simulation of such scenarios provides valuable data for AI-driven defense mechanisms. Candidate Tasks: Model different 6G attack vectors; implement scenarios in a simulator or testbed; analyze the impact on confidentiality, integrity, and availability; prepare datasets for ML-based defense methods. Tools / Skills: ns-3, OpenAirInterface, USRP (SDR), cellular security basics
Centralized vs. Decentralized AI Approaches for Network Security, MASTER Thesis Context & Motivation: Deciding whether AI for network security runs centrally (cloud) or decentrally (edge) affects performance, latency, and resilience. Scientific Relevance: The trade-off between cloud-based and edge AI is a hot research topic in 6G and has direct implications for secure infrastructures. Candidate Tasks: Compare centralized vs. decentralized AI security models; build proof-of-concepts (edge training vs. cloud training); evaluate performance, latency, and energy efficiency; derive architectural recommendations. Tools / Skills: TensorFlow Lite, Docker/Kubernetes, edge computing basics, distributed systems
Quantum-Based Untraceable Communication
Implementation and Benchmarking of Classical Traffic Morphing Techniques, BACHELOR Thesis
Classical traffic morphing techniques remain foundational for traffic privacy. Their performance and limits need
to be understood before integrating quantum-inspired ideas.
The thesis involves implementing traffic morphing strategies (e.g., dummy traffic, timing shifts), running
benchmark experiments, and evaluating their effectiveness under known traffic analysis attacks.
Frontend for Visualizing Simulated Traffic Traces, BACHELOR Thesis
Traffic simulations often generate abstract trace files that are hard to interpret. A visual frontend helps
researchers understand how protocols affect observable traffic.
The thesis includes developing a simple web-based interface for viewing packet timelines, flow
diagrams, and adversarial trace reconstructions based on simulation output files.
Integration of Adversarial Correlation Attacks into Simulation Testbed, MASTER Thesis
To evaluate metadata leakage, simulation environments must include realistic adversarial models capable of
timing and flow analysis.
The thesis will implement correlation-based traffic analysis attacks (e.g., flow linking, burst timing) as
modular components in the simulation framework, and provide evaluation scenarios comparing defenses from the
SQUID project.