Open Access

Dramatic increases in the number of cyber security attacks and breaches toward businesses and organizations have been experienced in recent years. The negative impacts of these breaches not only cause the stealing and compromising of sensitive information, malfunctioning of network devices, disruption of everyday operations, financial damage to the attacked business or organization itself, but also may navigate to peer businesses/organizations in the same industry. Therefore, prevention and early detection of these attacks play a significant role in the continuity of operations in IT-dependent organizations. At the same time detection of various types of attacks has become extremely difficult as attacks get more sophisticated, distributed and enabled by Artificial Intelligence (AI). Detection and handling of these attacks require sophisticated intrusion detection systems which run on powerful hardware and are administered by highly experienced security staff. Yet, these resources are costly to employ, especially for small and medium-sized enterprises (SMEs). To address these issues, we developed an architecture -within the GLACIER project- that can be realized as an in-house operated Security Information Event Management (SIEM) system for SMEs. It is affordable for SMEs as it is solely based on free and open-source components and thus does not require any licensing fees. Moreover, it is a Self-Contained System (SCS) and does not require too much management effort. It requires short configuration and learning phases after which it can be self-contained as long as the monitored infrastructure is stable (apart from a reaction to the generated alerts which may be outsourced to a service provider in SMEs, if necessary). Another main benefit of this system is to supply data to advanced detection algorithms, such as multidimensional analysis algorithms, in addition to traditional SIEMspecific tasks like data collection, normalization, enrichment, and storage. It supports the application of novel methods to detect security-related anomalies. The most distinct feature of this system that differentiates it from similar solutions in the market is its user feedback capability. Detected anomalies are displayed in a Graphical User Interface (GUI) to the security staff who are allowed to give feedback for anomalies. Subsequently, this feedback is utilized to fine-tune the anomaly detection algorithm. In addition, this GUI also provides access to network actors for quick incident responses. The system in general is suitable for both Information Technology (IT) and Operational Technology (OT) environments, while the detection algorithm must be specifically trained for each of these environments individually.

Recent developments in the field of deep learning have shown promising advances for a wide range of historically difficult computer vision problems. Using advanced deep learning techniques, researchers manage to perform high-quality single-image super-resolution, i.e., increasing the resolution of a given image without major losses in image quality, usually encountered when using traditional approaches such as standard interpolation. This thesis examines the process of deep learning super-resolution using convolutional neural networks and investigates whether the same deep learning models can be used to increase OCR results for low-quality text images.

Die Konvergenz von Netzwerken ist ein zunehmender Trend im Bereich der Automatisierung. Immer mehr Anlagenbetreiber streben eine Vereinheitlichung der Netzwerke in ihren Anlagen an. Dies führt zu einer nahtlosen Netzwerkstruktur, einer vereinfachten Überwachung und einem geringeren Schulungsaufwand für das Personal, da nur eine einheitliche Netzwerktechnologie gehandhabt werden muss. Ethernet-APL ist ein Teil des Puzzles für ein solches konvergentes Netzwerk und unterstützt verschiedene Echtzeitprotokolle wie PROFINET, EtherNet, HART-IP sowie das Middleware-Protokoll OPC UA. Dieses Papier gibt einen Überblick über die Auswirkungen von Ethernet-APL-Feldgeräten auf die OT-Sicherheit und schlägt vor, wie die OT-Sicherheit für diese Geräte gewährleistet werden kann.

Visual effects and elements in video games and interactive virtual environments can be applied to transfer (or delegate) non-visual perceptions (e.g. proprioception, presence, pain) to players and users, thus increasing perceptual diversity via the visual modality. Such elements or efects are referred to as visual delegates (VDs). Current fndings on the experiences that VDs can elicit relate to specifc VDs, not to VDs in general. Deductive and comprehensive VD evaluation frameworks are lacking. We analyzed VDs in video games to generalize VDs in terms of their visual properties. We conducted a systematic paper analysis to explore player and user experiences observed in association with specifc VDs in user studies. We conducted semi-structured interviews with expert players to determine their preferences and the impact of VD properties. The resulting VD framework (VD-frame) contributes to a more strategic approach to identifying the impact of VDs on player and user experiences.

Network convergence is an increasing trend in the automation domain. More and more plant owners strive for a unification of networks in their plants. This yields a seamless network structure, simplified supervision, and reduced training effort for the personnel, as only one unified network technology needs to be handled. Ethernet-APL is one piece of the puzzle for such a converged network, supporting various real time protocols like PROFINET, EtherNet, HART-IP as well as the middleware protocol OPC UA. This paper gives an overview on the impact of Ethernet-APL field devices to OT security and proposes how to ensure OT security for them.