Publications 2022

Script list Publications

(1) A New Injection Threat on S7-1500 PLCs - Disrupting the Physical Process Offline
W. Alsabbagh, P. Langendörfer
IEEE Open Journal of the Industrial Electronics Society 3, 146 (2022)
Programmable Logic Controllers (PLCs) are increasingly connected and integrated into the Industrial Internet of Things (IIoT) for a better network connectivity and a more streamlined control process. But in fact, this brings also its security challenges and exposes them to various cyber-attacks targeting the physical process controlled by such devices. In this work, we investigate whether the newest S7 PLCs are vulnerable by design and can be exploited. In contrast to the typical control logic injection attacks existing in the research community, which require from adversaries to be online along the ongoing attack, this article introduces a new exploit strategy that aims at disrupting the physical process controlled by the infected PLC when adversaries are not connected neither to the target nor to its network at the point zero for the attack. Our exploit approach is comprised of two prime steps: 1) patching the PLC with a malicious Time-of-Day interrupt block once an attacker gains access to an exposed PLC, 2) triggering the interrupt at a later time on the attacker will, when he is disconnected to the system’s network. For a real attack scenario, we implemented our attack approach on a Fischertechnik training system based on S7-1500 PLC using the latest version of S7CommPlus protocol. Our experimental results showed that we could keep the patched interrupt block in idle mode and hidden in the PLC memory for a long time without being revealed before being activated at a specific date and time that the attacker wishes. Finally, we suggested some potential security recommendations to protect our industrial environments from such a threat.

(2) Return-Oriented Programming Gadget Catalog for the Xtensa Architecture
B. Amatov, K. Lehniger, P. Langendörfer
Proc. of the Sixth International Workshop on Security, Privacy and Trust in the Internet of Things (SPT-IoT), 655 (2022)

(3) Psychological Targeting: Nudge or Boost to Foster Mindful and Sustainable Consumption?
E. Hermann
AI & Society (2022)
(Kompetenzzentrum IHP/BTU)
Artificial intelligence offers not only unprecedented opportunities for business and marketing, but also for the promotion of social and environmental good. In this article, I propose that psychological targeting powered by artificial intelligence can foster mindful and sustainable consumption by tailoring appeals (informational nudges) or nurturing consumers’ competences (boosts), thereby accounting for the social-good-perspective on the development and deployment of artificial intelligence.

(4) Testing Smart Grid Scenarios with Small Volume Testbed and Flexible Power Inverter
M. Krysik, K. Piotrowski, K. Turchan
Energies (MDPI) 15, 428 (2022)
Growing penetration of Renewable Energy Sources (RES) due to transition to future smart grid requires a huge amount of power converters that participate in the power flow. Each of these devices needs the use of a complex control and communication system, thus a platform for testing real-life scenarios is necessary. Several test techniques have been so far proposed that are subject to a trade-off between cost, test coverage, and test fidelity. This paper presents an approach for testing microgrids, by developing an emulator, with emphasis on the micro inverter unit and the possibility of flexible configuration for different grid topologies. In contrast to other approaches, our testbed is characterized by small volume and significantly scaled-down voltages for safety purposes. The test scenarios include behaviors in case of load changes, transition between grid-tied and islanded mode, connection and removal of subsequent inverters, and prioritization of inverters.

(5) FPGA-based Realtime detection of Freezing of Gait of Parkinson Patients
P. Langer, A. Haddadi Esfahani, Z. Dyka, P. Langendörfer
Proc. 16th EAI International Conference on Body Area Networks (EAI BODYNETS 2021) 101 (2022)

(6) Kafka-ML: Connecting the Data Stream with ML/AI Frameworks
Ch. Martin, P. Langendörfer, P.S. Zarrin, M. Diaz, B. Rubio
Future Generation Computer Systems 126, 15 (2022)
DOI: 10.1016/j.future.2021.07.037
Machine Learning (ML) and Artificial Intelligence (AI) depend on data sources to train, improve, and make predictions through their algorithms. With the digital revolution and current paradigms like the Internet of Things, this information is turning from static data to continuous data streams. However, most of the ML/AI frameworks used nowadays are not fully prepared for this revolution. In this paper, we propose Kafka-ML, a novel and open-source framework that enables the management of ML/AI pipelines through data streams. Kafka-ML provides an accessible and user-friendly Web user interface where users can easily define ML models, to then train, evaluate, and deploy them for inferences. Kafka-ML itself and the components it deploys are fully managed through containerization technologies, which ensure their portability, easy distribution, and other features such as fault-tolerance and high availability. Finally, a novel approach has been introduced to manage and reuse data streams, which may eliminate the need for data storage or file systems.

(7) Towards a Multisensor Station for Automated Biodiversity Monitoring
K. Piotrowski, J.W. Wägele, P. Bodesheim, S.J. Bourlat, J. Denzler, M. Diepenbroek, V. Fonseca, K.-H. Frommolt, M.F. Geiger, B. Gemeinholzer, F.O. Glöckner, T. Haucke, A. Kirse, A. Kölpin, I. Kostadinov, H.S. Kühl, F. Kurth, M. Lasseck, S. Liedke, F. Losch, S. Müller, N. Petrovskaya, B. Radig, Ch. Scherber, L. Schoppmann, J. Schulz, V. Steinhage, G.F. Tschan, W. Vautz, D. Velotto, M. Weigend, St. Wildermann
Basic and Applied Ecology 59, 105 (2022)
Rapid changes of the biosphere observed in recent years are caused by both small and large scale drivers, like shifts in temperature, transformations in land-use, or changes in the energy budget of systems. While the latter processes are easily quantifiable, documentation of the loss of biodiversity and community structure is more difficult. Changes in organismal abundance and diversity are barely documented. Censuses of species are usually fragmentary and inferred by often spatially, temporally and ecologically unsatisfactory simple species lists for individual study sites. Thus, detrimental global processes and their drivers often remain unrevealed. A major impediment to monitoring species diversity is the lack of human taxonomic expertise that is implicitly required for large-scale and fine-grained assessments. Another is the large amount of personnel and associated costs needed to cover large scales, or the inaccessibility of remote but nonetheless affected areas.
To overcome these limitations we propose a network of Automated Multisensor stations for Monitoring of species Diversity (AMMODs) to pave the way for a new generation of biodiversity assessment centers. This network combines cutting-edge technologies with biodiversity informatics and expert systems that conserve expert knowledge. Each AMMOD station combines autonomous samplers for insects, pollen and spores, audio recorders for vocalizing animals, sensors for volatile organic compounds emitted by plants (pVOCs) and camera traps for mammals and small invertebrates. AMMODs are largely self-containing and have the ability to pre-process data (e.g. for noise filtering) prior to transmission to receiver stations for storage, integration and analyses. Installation on sites that are difficult to access require a sophisticated and challenging system design with optimum balance between power requirements, bandwidth for data transmission, required service, and operation under all environmental conditions for years. An important prerequisite for automated species identification are databases of DNA barcodes, animal sounds, for pVOCs, and images used as training data for automated species identification. AMMOD stations thus become a key component to advance the field of biodiversity monitoring for research and policy by delivering biodiversity data at an unprecedented spatial and temporal resolution.

(8) Elastic Energy Management Algorithm using IoT Technology for Devices with Smart Appliance Functionality to React to the Occurrence of Overvoltage in the Smart Grid
P. Powroznik, P. Szczesniak, K. Piotrowski
Energies (MDPI) 15, 109 (2022)
(ebalance plus)
Currently, ensuring the correct functioning of the electrical grid is an important issue in terms of maintaining the normative voltage parameters and local line overloads. The unpredictability of renewable energy sources, the occurrence of the phenomenon of peak demand as well as the exceeding of the voltage level above the nominal values in the smart grid makes it justified to conduct further research in this field. The article presents the results of simulation tests and experimental laboratory tests of an electricity management system in order to reduce excessively high grid load or reduce excessively high grid voltage values resulting from increased production of prosumer RES. The research was based on the Elastic EnergyManagement algorithm for devices using IoT technology. The data for the algorithm was obtained from a message broker that implements the MQTT protocol. The presented results of the simulation and experiment confirmed the possibility of regulating the network voltage by the Elastic Energy Management algorithm in the event of voltage fluctuations related to excessive load or local generation.

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