In 2016, Google introduced the concept of Federated Learning (FL), enabling collaborative Machine Learning (ML). FL does not share local data but ML models, offering applications in diverse domains. This paper focuses on the application of FL to Intrusion Detection Systems (IDSs). There, common criteria to compare existing solutions are missing. In particular, this survey shows: (i) how FL-based IDSs are used in different domains; (ii) what differences exist between architectures; (iii) the state of the art of FL-based IDS. With a structured literature survey, this work identifies the relevant state of the art in FL–based intrusion detection from its creation in 2016 until 2021. It provides a reference architecture and a taxonomy to serve as guidelines to compare and design FL-based IDSs. Both are validated with the existing works. Finally, it identifies research directions for the application of FL to intrusion detection systems.

The growing prevalence of cybersecurity threats is a significant concern for railway systems, which rely on an extensive network of onboard and trackside sensors. These threats have the potential to compromise the safety of railway operations and the integrity of the railway infrastructure itself. This paper aims to examine the current cybersecurity measures in use, identify the key vulnerabilities that they address, and propose solutions for enhancing the security of railway infrastructures. The report evaluates the effectiveness of existing security protocols by reviewing current standards, including IEC62443 and NIST, as well as case histories of recent rail cyberattacks. Significant gaps have been identified, especially where modern and legacy systems need to be integrated. Weaknesses in communication protocols such as MVB, CAN and TCP/IP are identified. To address these challenges, the paper proposes a layered security framework specific to railways that incorporate continuous monitoring, risk-based cybersecurity modeling, AI-assisted threat detection, and stronger authentication methodologies. The aim of these recommendations is to improve the resilience of railway networks and ensure a safer, more secure infrastructure for future operations.

With the Internet of Things, more and more devices become remotely manageable. The amount and heterogeneity of managed devices make the task of implementing management functionality challenging. Future Pervasive Computing scenarios require implementing a plethora of services to provide management functionality. With growing demand on services, reducing the emerging complexity becomes increasingly important. A simple-to-use programming model for implementing complex management scenarios is essential to enable developers to create the growing amount of required management software at high quality. The paper presents how data-centric mechanisms, as known from network management, can be utilized to create a service-oriented architecture (SOA) for management services. The resulting shift of complexity from access functionality towards data structures introduces new flexibility and facilitates the programming of management applications significantly. This is evaluated with a user study on the reference implementation.

Besides technical skills, industry increasingly demands professional skills. Teaching these at university is challenging and still rare. In computer science, new technologies and relevant tools emerge quickly. Keeping curricula up to date is challenging. We present a teaching methodology that teaches students the art of teaching. Students learn how to communicate technical content efficiently in lecture, self-learning, and teamwork settings. Students work with constructive alignment and active listening. In addition, they learn how to assess self-learning training materials systematically, and how to give constructive feedback. Finally, they access a technical topic from a new dimension by practically teaching it. During the course, the students train the described skills under guidance by developing new exercises. The article presents our course unit that teaches important professional skills and produces a continuous stream of new teaching material, which is integrated into class. The unit enables teaching relevant and new technologies early. The presented methodology has been continuously applied at the Technical University of Munich since 2013. Through 2019, more than 150 students have participated. More than 60 new exercises were created. A structured evaluation shows the power of the approach.

This paper presents an in-depth study of Slow Denial of Service (DoS) attacks on MQTT, a widely used com- munication protocol in loT networks, building upon the existing literature. Our research focuses on extending the capabilities of such attacks, successfully lifting the simultaneous client limit, and achieving more than 25,000 active connections. This increase resulted in a notable escalation in resource consumption, underscoring the potential vulnerabilities in MQTT-based loT systems. Furthermore, we have implemented a Distributed DoS (DDoS) variant of the attack for the first time by randomizing source IP addresses and simulating packets originating from multiple sources. This approach complicates the detection and mitigation efforts by Intrusion Detection Systems, revealing additional weaknesses in loT security. The findings emphasize the importance of developing robust security mechanisms and strategies to protect against Slow DoS and DDoS attacks in resource-constrained loT environments. This study contributes to the growing body of research in loT security, particularly concerning the MQTT protocol. By addressing these security challenges, we aim to facilitate loT deployments' continued growth and success across various domains.