This demonstrator showcases three different methods to realize key functionalities of a multi-provider check-in/out transportation system such as billing, clearance, payments, and further data analytics in a privacy-preserving and scalable way. Our distributed architecture based on trusted execution environments can handle the check-in/out volume of a Europe-scale system even under server dropouts. Our architecture based on secure multi-party computation shows that very strong privacy guarantees for a city-scale system are achievable. With our payment channel network-based protocol, we demonstrate how instantaneous payment and clearing with formally verifiable security can be realized.

Continuous analysis of software system requirements, design, and implementation is important to detect vulnerabilities in EV charging units to ensure security.

The “NoPhish Concept and Awareness Measures” demonstrator provides background information about the Human and Societal Research Group and their applied research methods. It showcases four developed anti-phishing awareness measures: the NoPhish videos, two serious games for different target audiences, and the Security Teaching & Awareness Robot (STAR). Additionally, the demonstrator highlights the implementation of the NoPhish concept and its measures within the scientific community, organizations, and by end-users. Notably, 11 federal institutions, including the BSI, and 29 research institutions have adopted and/or recommended the NoPhish measures, and the videos have garnered over 30,000 views on YouTube.

FENCE is a cybersecurity research platform bridging theoretical methods and practical security implementations within the energy sector. Built upon a realistic infrastructure at KIT‘s Campus North – the KASTEL Security Lab Energy – it comprises several subsystems modeling renewable energy plants, multi-vendor digital substations, software-defined network setups, and PLC-based power plant simulations. We demonstrate a multi-stage cyber-attacks exploiting vulnerabilities in the Siemens S7 protocol, widely used in industrial control systems of power plants. FENCE facilitates a thorough analysis of vulnerabilities, supports research in intrusion detection, and enables effective mitigation strategies and systematic risk assessment. It specifically targets critical cybersecurity challenges within energy systems and presents our research findings through an intuitive web interface for enhanced accessibility and usability.

“Attacks on Traffic Light Recognition” demonstrates practical real-world attacks against neural networks in autonomous driving (AD). By exploiting backdoor and inference time attacks, an adversary can manipulate the perception module’s predictions, resulting in hazardous actions – such as running red lights. While such attacks were previously demonstrated using offline datasets, we are the first to effectively compromise a full-fledged autonomous vehicle in real-world conditions. Our research demonstrates that attacks against camera-based perception in AD are practical. To mitigate these threats, we explore the security of XAI-based defenses and propose anti-backdoor learning techniques.

The demonstrator visualizes proposed smart city sensors such as thermal and depth cameras, lidar and radar in real-time and thus highlights their privacy risks.

Continuous Automated Risk Management (CARM) is a non-intrusive industrial network security monitoring framework designed to assist Asset Owners of operational industrial systems in managing risks and developing the IEC 62443-mandated security architecture. CARM automates system information collection, assesses security posture, and measures descriptive security metrics using machine learning and graph theory-based analyses. It also helps select the optimal set of technical countermeasures while considering constraints such as budget limitations.