http://cyber-defence-campus.github.io/projects/topics/Recent content in Available project topics on Research @ Cyber-Defence CampusHugo -- gohugo.ioen-ushttp://cyber-defence-campus.github.io/projects/topics/topic_JAJU_2025_TM_Metrics/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_JAJU_2025_TM_Metrics/This project proposes a comprehensive investigation into the metrics and methodologies used to define EDTs. By exploring a range of scientific, economic, and strategic criteria, the project will develop a robust framework that can be applied across sectors to identify and monitor technologies with the potential to disrupt national security landscapes. Objectives Investigate existing frameworks, metrics, and methodologies used in various industries, academia, and by national security organizations to define EDTs. Propose a set of comprehensive, multi-dimensional metrics that combine factors such as technology maturity, adoption rate, potential impact, and level of disruption. Develop methodologies for evaluating the long-term risks and opportunities posed by EDTs, particularly in the context of national security.http://cyber-defence-campus.github.io/projects/topics/topic_JAJU_2025_TM_AI_Forecast/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_JAJU_2025_TM_AI_Forecast/Traditional approaches to identifying and mitigating threats in national security often lag behind the pace of technological advancements. Emerging disruptive technologies (EDTs) such as AI, 5G/6G, and quantum computing can outpace conventional defenses. As EDTs continue to evolve, there is an urgent need for more agile and predictive systems capable of identifying these developments before they manifest as full-scale threats. This research seeks to address this gap by utilizing AI-powered models to analyze vast datasets and generate forecasts on potential technological disruptions in the cyber defense landscape. Objectives Develop AI-Powered Forecasting Models: Utilize AI techniques (e.g., NLP, LLMs, LSTM etc.) to forecast the emergence of disruptive technologies that could impact national security.http://cyber-defence-campus.github.io/projects/topics/topic_2023_34/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_2023_34/http://cyber-defence-campus.github.io/projects/topics/topic_JAJU_2025_Online/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_JAJU_2025_Online/Cybersecurity professionals and researchers are continually challenged by the rapid pace at which new cyber threats and defense techniques emerge. Traditional channels for information dissemination, such as academic publications or industry reports, often lag behind real-time discussions happening on social media platforms. There is a critical need for tools and methodologies that can monitor and analyze social media content to detect emerging trends in cyber defense topics. This research seeks to fill that gap by leveraging data from social media to identify emerging themes, tools, and threats in the field of cybersecurity. Objectives Develop a Framework for Social Media Analysis: Build a robust framework to collect, process, and analyze data from social media content, focusing on cybersecurity discussions.http://cyber-defence-campus.github.io/projects/topics/topic_HULL_2024_Automated-AV-EDR-Evasion/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_HULL_2024_Automated-AV-EDR-Evasion/In today’s pentest and red teaming engagements, AV and EDR solutions are one of the most common and annoying obstacles to gaining an initial foothold in the target systems. Nevertheless, these solutions can usually be bypassed using sophisticated or customized malware/hacking tools. But this approach often takes a lot of time. Objectives The aim of the work is to automatically transform existing artifacts (source code, shellcode, binaries) in a way that they will bypass existing antivirus and EDR solutions. It is important that not only classic pattern matching but also behavioral detection is bypassed. We are open with regard to the proposed approach, provided it contains sufficient novelty and is useful against modern AV and EDR solutions.http://cyber-defence-campus.github.io/projects/topics/topic_ROME_2023_BT_Automation/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_ROME_2023_BT_Automation/This topic is currently reserved. The goal of this project is to work towards a fully automated player in cyber-defence exercises. We have been working on this topic for several years already (see publications below) but the there are still many open research challenges. If you are interested, please contact us and we will be happy to provide more details. Some of our past work in this area: Towards Generalizing Machine Learning Models to Detect Command and Control Attack Traffic [CyCon 2023] Towards an AI-powered Player in Cyber Defence Exercises [CyCon 2021] Machine Learning-based Detection of C&C Channels with a Focus on the Locked Shields Cyber Defense Exercise [CyCon 2019] Availability: This topic is currently reservedhttp://cyber-defence-campus.github.io/projects/topics/topic_2023_36/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_2023_36/http://cyber-defence-campus.github.io/projects/topics/topic_DOLA_2024_LLM_prompt_multilang/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_DOLA_2024_LLM_prompt_multilang/http://cyber-defence-campus.github.io/projects/topics/topic_BUMA_2026_Unlinkability_and_Metadata/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_BUMA_2026_Unlinkability_and_Metadata/Background The Swiss e-ID is planed to go live in December 2026 and is based on self-sovereign identity (SSI) principles. A public beta of the future Swiss E-ID is already available. Similiary, The European Union plans to introduce SSI-based solutions for the European Digital Identity (EUDI) soon. A central privacy requirement in the Swiss e-ID is the unlinkability of subsequent anonymous credential presentations. That is, if the same e-ID is presented twice to a verifier, the verifier must not be able to correlate the presentations as originating from the same source. Of course, this makes only sense if the disclosed attributes of the credential do not allow for correlation in the first place, e.http://cyber-defence-campus.github.io/projects/topics/topic_BUMA_2026_JavaCard_Applet/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_BUMA_2026_JavaCard_Applet/Background The Swiss e-ID is planned to go live in early 2027 and is based on self-sovereign identity (SSI) principles. A public beta of the future Swiss E-ID is already available. Similiary, The European Union plans to introduce SSI-based solutions for the European Digital Identity (EUDI) soon. A central element of e-ID security is the use of hardware secure elements (SE) on mobile phones for storage of users’ private keys. SEs bind the identity to a device, as keys in the SE cannot be copied or extracted by malware. For supporting privacy-preserving presentations of e-ID credentials, advanced cryptographic schemes, such as BBS+, are discussed.http://cyber-defence-campus.github.io/projects/topics/topic_2023_27/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_2023_27/Inferring causality from observational data is probably the most persistent problem in data analysis. Frankly speaking, our old ways of turning data analysis into causal explanations was a bit naive. We know that correlation is not causation, but we effectively ignored that distinction. At best, we admitted that we were uncertain and compared our results to the intuition to build confidence in our conclusions. In this project proposal we focus on causal relationships among time series. The Armed Conflict Location & Event Data Project (ACLED) [1] provides information about different conflict types around the world. Protests, social riots, and further conflict (sub-)types are collected and processed in a timely manner.http://cyber-defence-campus.github.io/projects/topics/topic_DOLA_2024_LLMcode_vulnerability/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_DOLA_2024_LLMcode_vulnerability/http://cyber-defence-campus.github.io/projects/topics/topic_JAJU_2025_TM_ChatGPT_Eng/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_JAJU_2025_TM_ChatGPT_Eng/The increasing sophistication of cyber threats demands innovative approaches to enhance cyber defense strategies. This research project aims to explore the potential role of ChatGPT and other large language models (LLMs) in advancing cyber defense capabilities. Specifically, the project will focus on the development and application of prompt engineering techniques to optimize ChatGPT’s use for various cyber defense tasks, such as early identification of disruptive technological breakthroughs, detection of emerging cyber threats, and enhancing situational awareness. By fine-tuning ChatGPT prompts, the research seeks to determine how LLMs can effectively contribute to the development of advanced cyber defense tools and strategies. The outcomes of this study will provide valuable insights into the integration of AI-driven solutions within cybersecurity practices.http://cyber-defence-campus.github.io/projects/topics/topic_JAJU_TM_AI_EDT/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_JAJU_TM_AI_EDT/Conventional threat detection methods rely heavily on historical data and signature-based detection, which are limited in their ability to detect novel or evolving cyber threats. As cyber-attacks become more dynamic and sophisticated, there is a pressing need for approaches that can anticipate, track, and analyze emerging threats in real-time. Data science approaches, particularly AI and ML, offer powerful tools for analyzing vast amounts of data, recognizing patterns, and predicting future threats. This research seeks to develop and apply AI/ML techniques to track emerging threats, providing national cyber defense systems with the ability to preempt and mitigate cyber-attacks more effectively. Objectives Develop AI/ML Models to identify, classify, and predict emerging cyber threats based on large datasets.http://cyber-defence-campus.github.io/projects/topics/topic_2023_35/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_2023_35/http://cyber-defence-campus.github.io/projects/topics/topic_2023_71/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_2023_71/The task of geo- and chronolocation is to determine where and when a particular image or video was taken. The proposed project is part of a larger program, named Battle Damage Assessment (BDA), in which damages, caused by different war events, will be assessed using photographic material from a wide variety of sources, including satellite imagery. In this project, we will focus on image material from human operated cameras, typically images that are posted on open source social media or news platforms. If the photographic material is supplied with metadata about space and time, the task of geo- and chronolocation may be limited to the verification of the metadata labels.http://cyber-defence-campus.github.io/projects/topics/topic_2023_37/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_2023_37/http://cyber-defence-campus.github.io/projects/topics/topic_MASTR_2026_WirelessAviation/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_MASTR_2026_WirelessAviation/Legacy and novel technology for the communication of aircraft and unmanned aerial vehicles (UAV) abound. Security issues have been shown for example in FLARM [1], TCAS [2], MLAT [3] and practically all other deployed legacy systems. Interference with GPS systems is a widespread daily occurance for any aircraft transiting wide conflict zones. [4] Multilateration (MLAT) can determine the position of a transmitter using observations from geographically distributed receivers. It is an important independent complement to self-reported aircraft positions, but several practical challenges remain when signals and receiver infrastructure are incomplete or unreliable. We offer two related Master thesis topics addressing these challenges using data and infrastructure from the OpenSky Network (https://opensky-network.http://cyber-defence-campus.github.io/projects/topics/topic_2023_28/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_2023_28/Disruptive events are in the focus of this project. Of particular interest are geopolitical events that have a large impact due to their extents and sudden occurrence. Smoldering conflicts that suddenly turn into waves of protest, social unrest or even warlike events are examples of such disruptive developments. The advantages of being able to predict such disruptive changes are obvious. Important for the predictive capability is to understand the diverse mechanisms that can lead to disruptive behaviors. In dynamic and typically non-linear systems - including societal systems - disruptive events often arise due to so-called tipping point dynamics. After reaching a certain threshold, i.http://cyber-defence-campus.github.io/projects/topics/topic_NOBE_2025_NFC-access-control-analysis/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_NOBE_2025_NFC-access-control-analysis/Overview Access control systems are widely deployed in buildings across the globe to secure physical access and restrict unauthorized entry. Many of those solutions are using NFC technologies such as ISO14443 or ISO15693. There are plenty of solutions which are usually not publicly documented, and some of them have been publicly broken despite the lack of documentation. However, some solutions don’t appear to have the same level of scrutinity. Objective The goal of this Master’s thesis is to conduct an in-depth security assessment of a specific solution with the aim of: Understanding the internal working and mode of operation of the system Documenting the communication protocols and encryption mechanisms involved Exploring potential vulnerabilities in the implementation.http://cyber-defence-campus.github.io/projects/topics/topic_MASTR_2026_PLC/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_MASTR_2026_PLC/In recent years, the CYD Campus and other researchers have found several critical vulnerabilities in the powerline communication (PLC) system used in a wide array of infrastructure from electric vehicles, private homes to industrial automation. The goal of this project is to extend the state of the art attacks, such as Brokenwire [1] and Eavesdropping attacks [2] and build a cost-effective off-the-shelf wireless PLC sniffer. This sniffer will then be used to conduct security analyses of specific PLC deployments (such as the CYD Campus domotics laboratory) as well as a large-scale study of the state of the PLC infrastructure. Required Skills: Signal processing Pro­gramming in Py­thon/C Some fa­mi­li­a­ri­ty with software-defined radio [1] Brokenwire: Wireless disruption of CCS electric vehicle charging S Köhler, R Baker, M Strohmeier, I Martinovic The 30th Network and Distributed System Security Symposium (NDSS), 2023 https://brokenwire.http://cyber-defence-campus.github.io/projects/topics/topic_MASTR_2026_Train/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_MASTR_2026_Train/Rolling stock is a critical infrastructure that requires high levels of safety and security. While a lot of research has been conducted previously in other critical infrastructure domains such as aerospace, cars or industrial control systems, rolling stock and train systems have been neglected so far, mainly due to their inaccessibility. This thesis will be conducted in collaboration with a train operator. The Train Control and Management System (TCMS) and Automatic Train Control (ATC) systems are the backbone of on- board rolling stock networks, and their security is of utmost importance. Intrusion detection systems are commonly used to detect malicious activity on computer networks, but traditional methods may not be suitable for rolling stock.http://cyber-defence-campus.github.io/projects/topics/topic_MASTR_2026_SmartTacho/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_MASTR_2026_SmartTacho/Smart tachographs combine vehicle units, personal smart cards, satellite positioning, short-range communication, public-key infrastructure, and backend services to support regulatory compliance in European road transport. This creates a complex cyber-physical ecosystem in which security, privacy, interoperability, and operational reliability are closely connected. European Commission JRC overview This MSc thesis will investigate the security and resilience of selected components and interactions within the SmartTacho ecosystem. The work may include protocol and trust analysis, practical experiments in a controlled environment, and the development of tools for testing or monitoring relevant system properties. This concerns in particular the wireless aspects of the systems and standards (e.http://cyber-defence-campus.github.io/projects/topics/topic_HULL_2025_Security-of-DJI-Drone-Firmwares/Mon, 01 Jan 0001 00:00:00 +0000http://cyber-defence-campus.github.io/projects/topics/topic_HULL_2025_Security-of-DJI-Drone-Firmwares/Modern drones are increasingly complex networked systems whose firmware plays a critical role in flight safety and data integrity. This project seeks to assess the security of DJI drone firmware to better understand current risks and evaluate the effectiveness of existing protective mechanisms. Objectives extract firmware encryption keys or dump unextracted firmwares analyze firmware for vulnerabilities (vuln research) analyze use of TEE for firmware integrity with the goal to potentially beeing able to modify firmware images. Requirements Hardware security. Understanding of the AAarch64 architecture. Experience in reverse engineering. Mindset to learn the additional skills.