TY - GEN A1 - Petryk, Dmytro A1 - Dyka, Zoya A1 - Langendörfer, Peter T1 - Laser Fault Injection Attacks against IHP Chips T2 - Proc. 32. Crypto-Day Matters 2021, (2021) Y1 - 2021 U6 - https://doi.org/10.18420/cdm-2021-32-22 ER - TY - GEN A1 - Alsabbagh, Wael A1 - Langendörfer, Peter T1 - A Stealth Program Injection Attack against S7-300 PLCs T2 - 2021 22nd IEEE International Conference on Industrial Technology (ICIT) Y1 - 2021 SN - 978-1-7281-5731-3 SN - 978-1-7281-5730-6 U6 - https://doi.org/10.1109/ICIT46573.2021.9453483 ER - TY - GEN A1 - Frohberg, Max A1 - Weidling, Stefan A1 - Langendörfer, Peter T1 - Challenges in Developing a Wireless Sensor Network for an Agricultural Monitoring and Decision System T2 - Selected Papers from the 12th International Networking Conference, INC 2020 Y1 - 2021 SN - 978-3-030-64757-5 SN - 978-3-030-64760-5 SN - 978-3-030-64758-2 U6 - https://doi.org/10.1007/978-3-030-64758-2_16 SN - 2367-3370 SP - 224 EP - 240 PB - Springer CY - Cham ER - TY - GEN A1 - Kabin, Ievgen A1 - Dyka, Zoya A1 - Klann, Dan A1 - Aftowicz, Marcin A1 - Langendörfer, Peter T1 - FFT based Horizontal SCA Attack against ECC T2 - 2021 11th IFIP International Conference on New Technologies, Mobility and Security (NTMS) Y1 - 2021 SN - 978-1-6654-4400-2 U6 - https://doi.org/10.1109/NTMS49979.2021.9432665 SN - 2157-4960 ER - TY - GEN A1 - Haddadi Esfahani, Ali A1 - Dyka, Zoya A1 - Ortmann, Steffen A1 - Langendörfer, Peter T1 - Impact of Data Preparation in Freezing of Gait Detection using Feature-Less Recurrent Neural Network T2 - IEEE Access Y1 - 2021 U6 - https://doi.org/10.1109/ACCESS.2021.3117543 SN - 2169-3536 IS - 9 SP - 138120 EP - 138131 ER - TY - GEN A1 - Kabin, Ievgen A1 - Dyka, Zoya A1 - Klann, Dan A1 - Aftowicz, Marcin A1 - Langendörfer, Peter T1 - Resistance of the Montgomery Ladder against Simple SCA: Theory and Practice T2 - Journal of Electronic Testing Y1 - 2021 U6 - https://doi.org/10.1007/s10836-021-05951-3 SN - 1573-0727 SN - 0923-8174 VL - 37 IS - 3 SP - 289 EP - 303 ER - TY - GEN A1 - Assafo, Maryam A1 - Langendörfer, Peter T1 - A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools under Different Operating Conditions T2 - IEEE Access Y1 - 2021 U6 - https://doi.org/10.1109/ACCESS.2021.3091476 SN - 2169-3536 IS - 9 SP - 90011 EP - 90028 ER - TY - GEN A1 - Petryk, Dmytro A1 - Dyka, Zoya A1 - Pérez, Eduardo A1 - Kabin, Ievgen A1 - Katzer, Jens A1 - Schäffner, Jan A1 - Langendörfer, Peter T1 - Sensitivity of HfO2-based RRAM Cells to Laser Irradiation T2 - Microprocessors and Microsystems Y1 - 2021 U6 - https://doi.org/10.1016/j.micpro.2021.104376 SN - 0141-9331 IS - 87 ER - TY - GEN A1 - Vogel, Elisabeth A1 - Dyka, Zoya A1 - Klann, Dan A1 - Langendörfer, Peter T1 - Resilience in the Cyber World: Definitions, Features and Models T2 - Future Internet Y1 - 2021 U6 - https://doi.org/10.3390/fi13110293 SN - 1999-5903 VL - 13 IS - 11 ER - TY - GEN A1 - Kabin, Ievgen A1 - Dyka, Zoya A1 - Klann, Dan A1 - Schäffner, Jan A1 - Langendörfer, Peter T1 - On the Complexity of Attacking Commercial Authentication Products T2 - Microprocessors and Microsystems Y1 - 2021 U6 - https://doi.org/10.1016/j.micpro.2020.103480 SN - 0141-9331 IS - 80 ER - TY - GEN A1 - Assafo, Maryam A1 - Städter, Jost Philipp A1 - Meisel, Tenia A1 - Langendörfer, Peter T1 - On the Stability and Homogeneous Ensemble of Feature Selection for Predictive Maintenance: A Classification Application for Tool Condition Monitoring in Milling T2 - Sensors N2 - Feature selection (FS) represents an essential step for many machine learning-based predictive maintenance (PdM) applications, including various industrial processes, components, and monitoring tasks. The selected features not only serve as inputs to the learning models but also can influence further decisions and analysis, e.g., sensor selection and understandability of the PdM system. Hence, before deploying the PdM system, it is crucial to examine the reproducibility and robustness of the selected features under variations in the input data. This is particularly critical for real-world datasets with a low sample-to-dimension ratio (SDR). However, to the best of our knowledge, stability of the FS methods under data variations has not been considered yet in the field of PdM. This paper addresses this issue with an application to tool condition monitoring in milling, where classifiers based on support vector machines and random forest were employed. We used a five-fold cross-validation to evaluate three popular filter-based FS methods, namely Fisher score, minimum redundancy maximum relevance (mRMR), and ReliefF, in terms of both stability and macro-F1. Further, for each method, we investigated the impact of the homogeneous FS ensemble on both performance indicators. To gain broad insights, we used four (2:2) milling datasets obtained from our experiments and NASA’s repository, which differ in the operating conditions, sensors, SDR, number of classes, etc. For each dataset, the study was conducted for two individual sensors and their fusion. Among the conclusions: (1) Different FS methods can yield comparable macro-F1 yet considerably different FS stability values. (2) Fisher score (single and/or ensemble) is superior in most of the cases. (3) mRMR’s stability is overall the lowest, the most variable over different settings (e.g., sensor(s), subset cardinality), and the one that benefits the most from the ensemble. KW - classification KW - feature selection KW - homogeneous feature selection ensemble KW - predictive maintenance KW - milling KW - sensor fusion KW - stability of feature selection KW - tool condition monitoring Y1 - 2023 U6 - https://doi.org/10.3390/s23094461 SN - 1424-8220 VL - 23 IS - 9 ER - TY - GEN A1 - Alsabbagh, Wael A1 - Langendörfer, Peter T1 - A New Injection Threat on S7-1500 PLCs - Disrupting the Physical Process Offline T2 - IEEE Open Journal of the Industrial Electronics Society Y1 - 2022 U6 - https://doi.org/10.1109/OJIES.2022.3151528 SN - 2644-1284 IS - 3 SP - 146 EP - 162 ER - TY - GEN A1 - Kabin, Ievgen A1 - Dyka, Zoya A1 - Langendörfer, Peter T1 - Atomicity and Regularity Principles do not Ensure Full Resistance of ECC Designs against Single-Trace Attacks T2 - Sensors Y1 - 2022 U6 - https://doi.org/10.3390/s22083083 SN - 1424-8220 VL - 22 IS - 8 ER - TY - GEN A1 - Lehniger, Kai A1 - Saad, Abdelaziz A1 - Langendörfer, Peter T1 - Finding Gadgets in Incremental Code Updates for Return-Oriented Programming Attacks on Resource-Constrained Devices T2 - Annals of Telecommunications Y1 - 2022 U6 - https://doi.org/10.1007/s12243-022-00917-8 SN - 0003-4347 VL - 78 SP - 209 EP - 229 ER - TY - GEN A1 - Martin, Cristian A1 - Langendörfer, Peter A1 - Zarrin, Pouya Soltani A1 - Díaz, Manuel A1 - Rubio, Bartolomé T1 - Kafka-ML: Connecting the Data Stream with ML/AI Frameworks T2 - Future Generation Computer Systems Y1 - 2022 U6 - https://doi.org/10.1016/j.future.2021.07.037 SN - 0167-739X VL - 126 SP - 15 EP - 33 ER - TY - GEN A1 - Amatov, Batyi A1 - Lehniger, Kai A1 - Langendörfer, Peter T1 - Return-Oriented Programming Gadget Catalog for the Xtensa Architecture T2 - 6th International Workshop on Security, Privacy and Trust in the Internet of Things (SPT-IoT 2022) Y1 - 2022 SN - 978-1-6654-1647-4 SN - 978-1-6654-1648-1 U6 - https://doi.org/10.1109/PerComWorkshops53856.2022.9767489 ER - TY - GEN A1 - Lang, Patrick A1 - Haddadi Esfahani, Ali A1 - Dyka, Zoya A1 - Langendörfer, Peter ED - Rehman, Masood Ur T1 - FPGA-based Realtime Detection of Freezing of Gait of Parkinson Patients T2 - Body Area Networks. Smart IoT and Big Data for Intelligent Health Management : 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings Y1 - 2022 SN - 978-3-030-95593-9 SN - 978-3-030-95592-2 U6 - https://doi.org/10.1007/978-3-030-95593-9_9 SN - 1867-8211 SN - 1867-822X SP - 101 EP - 111 PB - Springer International Publishing ER - TY - GEN A1 - Alsabbagh, Wael A1 - Langendörfer, Peter T1 - No Need to be Online to Attack - Exploiting S7-1500 PLCs by Time-Of-Day Block T2 - Proc. 28th International Conference on Information, Communication and Automation Technologies (ICAT 2022), arajevo, Bosnia and Herzegovina, 16-18 June 2022 Y1 - 2022 SN - 978-1-6654-6692-9 SN - 978-1-6654-6691-2 U6 - https://doi.org/10.1109/ICAT54566.2022.9811147 SN - 2643-1858 ER - TY - GEN A1 - Dyka, Zoya A1 - Kabin, Ievgen A1 - Brzozowski, Marcin A1 - Panic, Goran A1 - Calligaro, Cristiano A1 - Krstic, Milos A1 - Langendörfer, Peter T1 - On the SCA Resistance of Crypto IP Cores T2 - 23rd IEEE Latin-American Test Symposium (LATS 2022), Montevideo, Uruguay, 05-08 September 2022 Y1 - 2022 SN - 978-1-6654-5707-1 SN - 978-1-6654-5708-8 U6 - https://doi.org/10.1109/LATS57337.2022.9937007 SN - 2373-0862 ER - TY - GEN A1 - Shamilyan, Oxana A1 - Kabin, Ievgen A1 - Dyka, Zoya A1 - Langendörfer, Peter T1 - Distributed Artificial Intelligence as a Means to Achieve Self-X-Functions for Increasing Resilience: the First Steps T2 - 11th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro, 07-10 June 2022 Y1 - 2022 SN - 978-1-6654-6828-2 SN - 978-1-6654-6827-5 U6 - https://doi.org/10.1109/MECO55406.2022.9797193 SN - 2637-9511 ER - TY - GEN A1 - Aftowicz, Marcin A1 - Lehniger, Kai A1 - Langendörfer, Peter T1 - Scalable FPGA Hardware Accelerator for SVM Inference T2 - Proc. 10th International Conference on Cyber-Physical Systems and Internet-of-Things (CPS&IoT 2022) Y1 - 2022 SN - 978-1-6654-6828-2 SN - 978-1-6654-6827-5 U6 - https://doi.org/10.1109/MECO55406.2022.9797110 SN - 2637-9511 ER - TY - GEN A1 - Alsabbagh, Wael A1 - Langendörfer, Peter ED - Jasperneite, Jürgen ED - Lohweg, Volker T1 - A Remote Attack Tool on Siemens S7-300 Controllers: Practical Report T2 - Kommunikation und Bildverarbeitung in der Automation : Ausgewählte Beiträge der Jahreskolloquien KommA und BVAu 2020 Y1 - 2022 SN - 978-3-662-64282-5 SN - 978-3-662-64283-2 U6 - https://doi.org/10.1007/978-3-662-64283-2_1 SN - 2522-8579 SP - 3 EP - 21 PB - Springer Vieweg CY - Heidelberg ER - TY - GEN A1 - Lehniger, Kai A1 - Langendörfer, Peter T1 - Through the Window: On the Exploitability of Xtensa’s Register Window Overflow T2 - 32nd International Telecommunication Networks and Applications Conference (ITNAC 2022), Wellington, New Zealand, 30 November 2022 - 02 December 2022 Y1 - 2022 SN - 978-1-6654-7103-9 SN - 978-1-6654-7104-6 U6 - https://doi.org/10.1109/ITNAC55475.2022.9998407 SN - 2474-154X SP - 353 EP - 358 ER - TY - GEN A1 - Brzozowski, Marcin A1 - Langendörfer, Peter A1 - Casaca, Augusto A1 - Grilo, Antonio A1 - Diaz, Manuel A1 - Martín, Cristian A1 - Camacho, Jose A1 - Landi, Giada T1 - UNITE: Integrated IoT-Edge-Cloud Continuum T2 - 2022 IEEE 8th World Forum on Internet of Things (WF-IoT), Yokohama, 26 October 2022 - 11 November 2022 Japan Y1 - 2022 SN - 978-1-6654-9153-2 SN - 978-1-6654-9154-9 U6 - https://doi.org/10.1109/WF-IoT54382.2022.10152292 ER - TY - GEN A1 - Förster, Benjamin A1 - Hinze, Thomas A1 - Langendorfer, Peter T1 - Topology- and Resource-Based Distribution Scheme for Collaborative Security-Focused Design Space Exploration in Large-Scale Static WSNs T2 - 2024 34th International Telecommunication Networks and Applications Conference (ITNAC) Y1 - 2024 U6 - https://doi.org/10.1109/ITNAC62915.2024.10815368 SP - 1 EP - 6 PB - IEEE ER - TY - GEN A1 - Esfahani, Ali Haddadi A1 - Maye, Oliver A1 - Frohberg, Max A1 - Speh, Maria A1 - Jöbges, Micheal A1 - Langendörfer, Peter T1 - Real time detection of Freezing of Gait of Parkinson patients based on machine learning running on a body worn device T2 - Procedia Computer Science Y1 - 2024 U6 - https://doi.org/10.1016/j.procs.2024.06.160 SN - 1877-0509 VL - 239 SP - 177 EP - 184 PB - Elsevier BV ER - TY - GEN A1 - Alsabbagh, Wael A1 - Amogbonjaye, Samuel A1 - Kim, Chaerin A1 - Langendörfer, Peter T1 - Pirates of the MQTT : raiding IIoT systems with a rogue client T2 - 2024 8th Cyber Security in Networking Conference (CSNet) N2 - The integration of MQTT (Message Queuing Telemetry Transport) in Industrial Internet of Things (IIoT) systems enhances operational efficiency but introduces significant security vulnerabilities, particularly through rogue MQTT clients. These clients exploit MQTT weaknesses to disrupt industrial processes and compromise data integrity. This paper examines the impact of rogue MQTT client attacks, demonstrated through a detailed case study using the Fischertechnik Lernfabrik 4.0. We highlight how attackers can exploit MQTT's inherent vulnerabilities, including anonymous connections and message retention, to inject false data and interfere with operations. To address these risks, we propose several mitigation strategies: implementing fine-grained authorization for topic access control, enhancing replay attack protection with Message Authentication Code (MAC), employing mutual TLS (mTLS) for secure client authentication, and incorporating real-time client activity monitoring and anomaly detection. These measures aim to bolster IIoT system security, mitigate potential disruptions, and maintain operational integrity. Our findings and recommendations contribute to advancing security practices in MQTT-based IIoT environments. All attack codes and a proof-of-concept are ublicly available. KW - Rogue Client KW - MQTT Protocol KW - Cyberattacks KW - Cybersecurity Y1 - 2024 U6 - https://doi.org/10.1109/CSNet64211.2024.10851733 SP - 248 EP - 253 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Lehniger, Kai A1 - Langendörfer, Peter T1 - Comment on ‘‘RIO: Return Instruction Obfuscation for Bare-Metal IoT Devices’’ T2 - IEEE Access N2 - This is a comment on “RIO: Return Instruction Obfuscation for Bare-Metal IoT Devices.” RIO prevents finding gadgets for return-oriented programming attacks by encrypting return instructions. This paper shows flaws in the design of RIO that allow for the easy retrieval of the plaintext return instructions without decrypting them. Additionally, changes are proposed to improve upon the original idea. KW - ARM KW - Internet of Things KW - Return-oriented programming KW - Security Y1 - 2025 U6 - https://doi.org/10.1109/ACCESS.2025.3568598 SN - 2169-3536 VL - 13 SP - 90358 EP - 90361 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Alsabbagh, Wael A1 - Kim, Chaerin A1 - Langendörfer, Peter T1 - A payload of lies : false data injection attacks on MQTT-based IIoT systems T2 - IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society : proceedings N2 - In the ever-evolving landscape of Industrial Internet of Things (IIoT), security emerges as a critical concern. This paper delves into the realm of False Data Injection Attacks (FDIAs) within MQTT-based IIoT systems, specifically targeting the publisher-subscriber model. Our exploration unveils two distinct attack scenarios that exploit the vulnerabilities inherent in the communication fabric. In the first scenario, we demonstrate the potential chaos wrought by sending false data to subscribers, manipulating their perception and inducing actions that align with the attacker’s whims. The second scenario ventures into the heart of the publisher, where the attacker injects false data – deceptive status updates from other publishers e.g., Programmable Logic Controllers (PLCs). The repercussions ripple through the entire industrial process, impacting operations based on fraudulent information. This showcases the cascading effects of FDIAs, illustrating the profound threat they pose to the reliability and integrity of IIoT systems. For real-world attack scenarios, Our attacks were conducted on a small MQTT-based IIoT system, using the Fischertechnik Lernfabrik 4.0 9V factory. Finally, we proposes mitigation solutions to safeguard IIoT systems from the far-reaching consequences of false data manipulation. Our attack codes as well as a proof-of-concept are publicly available for further research. KW - FDIAs KW - IIoT KW - IoT KW - MQTT Protocol Y1 - 2024 SN - 978-1-6654-6454-3 U6 - https://doi.org/10.1109/IECON55916.2024.10905487 SP - 1 EP - 8 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Shamilyan, Oxana A1 - Kabin, Ievgen A1 - Dyka, Zoya A1 - Langendörfer, Peter T1 - Resilient movement planning for continuum robots T2 - MODeM 2024 : Multi-Objective Decision Making Workshop at ECAI 2024, 20 October 2024, Santiago de Compostela, Spain N2 - The paper presents an experimental study of resilient path planning for con-tinuum robots taking into account the multi-objective optimisation problem. To do this, we used two well-known algorithms, namely Genetic algorithm and A* algorithm, for path planning and the Analytical Hierarchy Process algorithm for paths evaluation. In our experiment Analytical Hierarchy Process algorithm considers four different criteria, i.e. distance, motors damage, mechanical damage and accuracy each considered to contribute to the resilience of a continuum robot. The use of different criteria is necessary to increasing the time to maintenance operations of the robot. The experiment shows that on the one hand both algorithms can be used in combination with Analytical Hierarchy Process algorithm for multi criteria path-planning, while Genetic algorithm shows superior performance in the comparison of the two algorithms. Y1 - 2024 UR - https://modem2024.vub.ac.be/papers/MODeM2024_paper_3.pdf U6 - https://doi.org/10.48550/arXiv.2404.06178 SP - 1 EP - 7 ER - TY - GEN A1 - Dietrich, Lucas A1 - Förster, Benjamin A1 - Langendörfer, Peter A1 - Hinze, Thomas T1 - A visual discrete event-based simulator for protection of plants against herbivores employed as computational optimization game N2 - Plants come with sophisticated strategies to survive within a highly competing environment. In addition, they need to resist frequent attacks from a variety of herbivores acting alone, in small groups, or in swarms. Since the amount of energy a plant might invest in defense and reproduction is limited, a complex optimization problem emerges. In a shared habitat, plants fight herbivores by shape and camouflage, by the release of specific toxins, or by attracting predators of herbivores. Furthermore, plants alert their surrounding field by signaling substances in the event of an assault. Transported by air or through a network of roots, signaling substances reach neighbors to trigger their defense. The offsprings of a plant commonly grow within a certain distance to benefit from symbiotic protection. We introduce a grid-based visual simulation software for detailed configuration and subsequent processing of the behavior of the resulting system in time and space. In terms of solution to a computational optimization problem inspired by nature, settings with low energy need and long life able to cope with different patterns of attack can be figured out and analyzed. Applications include novel techniques for efficient construction and secure operation of sensor networks. KW - Plant defense KW - Event-based simulation KW - Topological optimization Y1 - 2025 U6 - https://doi.org/10.48550/arXiv.2509.15787 SP - 1 EP - 25 PB - arXiv ER - TY - GEN A1 - Lehniger, Kai A1 - Langendörfer, Peter T1 - Investigating compact shadow stacks for the Xtensa LX architecture T2 - 2025 14th Mediterranean Conference on Embedded Computing (MECO) N2 - This paper presents a compact shadow stack implementation for the Xtensa LX architecture, implemented for an ESP32 microcontroller. The implementation is done by changing the register window exception handlers in the FreeRTOS operating system. In a second implementation the shadow stack was extended to all register values, not just return addresses. This not only increases security but also performance, reducing the overhead from 17.220% to 13.529%, in the worst-case-scenario. The paper also suggests future improvements that could decrease the overhead even further. KW - Xtensa KW - ESP32 KW - Return address protection KW - Shadow stack Y1 - 2025 SN - 979-8-3315-1341-2 U6 - https://doi.org/10.1109/MECO66322.2025.11049161 SP - 1 EP - 6 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Alsabbagh, Wael A1 - Sayegh, Bahij A1 - Kim, Chaerin A1 - Langendorfer, Peter T1 - FeatNet-IDS : anomaly detection based-features for industrial internet of things systems T2 - 2025 Silicon Valley Cybersecurity Conference (SVCC) N2 - Anomaly detection is critical for securing Industrial Internet of Things (IIoT) systems against evolving cyber threats. This paper introduces FeatNet-IDS, an enhanced Intrusion Detection System (IDS) for MQTT-based IIoT environments. FeatNet- IDS combines advanced feature selection and machine learning techniques to improve anomaly detection accuracy, efficiency, and interpretability. Evaluated on a domain-specific dataset of MQTT-related attacks, including control logic injection, the framework achieves 92.3% accuracy, 91.5% F1-score, and 96.8% ROC AUC, with a low false alarm rate of 2.73%. By emphasizing feature stability and importance, FeatNet-IDS outperforms or matches state-of-the-art methods, demonstrating robustness and transparency. Practical deployment implications are discussed, with future work addressing dataset expansion, hyperparameter tuning, and advanced ensemble approaches. The dataset and framework codes are publicly available to support further research. KW - IIoT KW - MQTT Protocol KW - Cyberattacks KW - Cybersecurity KW - Anomaly Detection Y1 - 2025 SN - 979-8-3315-3429-5 U6 - https://doi.org/10.1109/SVCC65277.2025.11133650 SP - 1 EP - 8 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Alsabbagh, Wael A1 - Urrego, Diego A1 - Langendörfer, Peter T1 - Smart traps for smart systems : scalable honeynets for IIoT cybersecurity T2 - 2025 34th International Conference on Computer Communications and Networks (ICCCN) N2 - Honeypots serve as decoy systems that attract and monitor intruders, offering insights into their behavior. When interconnected, these honeypots form honeynets, simulating high-value environments to engage attackers and facilitate deeper analysis. However, in Industrial Internet of Things (IIoT) networks, deploying honeypots presents challenges such as static configurations, lack of network context, and difficulties in scaling.In this paper, we introduce TrapNet, a scalable, lightweight honeynet framework specifically designed for IIoT environments. TrapNet combines compact, on-site honeypots with large-scale, adaptive honeynets deployed on fog and cloud infrastructures using microservices. This approach enables fast deployment, scalability, and flexibility, providing an effective solution for IIoT cybersecurity. Our experimental results show that TrapNet efficiently detects intrusions while maintaining low resource overhead. Additionally, all code and configurations used in this study are publicly available, fostering further research and innovation in honeynet design and IIoT security. KW - Honeypots KW - Cybersecurity KW - IIoT Security KW - Fog Computing KW - Cloud Infrastructure Y1 - 2025 SN - 979-8-3315-0898-2 U6 - https://doi.org/10.1109/ICCCN65249.2025.11133761 SP - 1 EP - 6 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Yener, Onur A1 - Brzozowski, Marcin A1 - Chitauro, Ronald A1 - Langendörfer, Peter T1 - Pushing the limits of 5G private networks : a practical examination of network stressors T2 - 2025 International Conference on Computing, Networking and Communications (ICNC) N2 - The adoption of 5G private networks is revolutionizing industrial connectivity, yet it brings numerous challenges that need addressing to ensure safety and reliability. Our research utilizes an OpenAirInterface (OAI) based 5G testbed to explore and mitigate these challenges. We particularly focus on stressors such as jamming to evaluate network resilience. Our methodology involves setting up a comprehensive testbed, collecting data under various stress conditions, and implementing solutions. Initial findings demonstrate that small environmental changes and structural interferences significantly impact network performance. We observed that indoor variables and uplink communication pose substantial challenges, underscoring the need for advanced algorithms to detect and mitigate jamming attacks in dynamic scenarios. Our results highlight the importance of real-time spectrum analysis and adaptive network management in maintaining optimal performance. This study aims to contribute to the development of robust, resilient 5G networks suitable for complex industrial environments. KW - 5G Private Networks KW - OpenAirInterface KW - Jamming KW - Network Stressors KW - Industrial Connectivity KW - Network Resilience Y1 - 2025 SN - 979-8-3315-2096-0 U6 - https://doi.org/10.1109/ICNC64010.2025.10993913 SP - 752 EP - 756 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Pidvalnyi, Illia A1 - Kostenko, Anna A1 - Sudakov, Oleksandr A1 - Isaev, Dmytro A1 - Maximyuk, Oleksandr A1 - Krishtal, Oleg A1 - Iegorova, Olena A1 - Kabin, Ievgen A1 - Dyka, Zoya A1 - Ortmann, Steffen A1 - Langendörfer, Peter T1 - Classification of epileptic seizures by simple machine learning techniques : application to animals’ electroencephalography signals T2 - IEEE access N2 - Detection and prediction of the onset of seizures are among the most challenging problems in epilepsy diagnostics and treatment. Small electronic devices capable of doing that will improve the quality of life for epilepsy patients while also open new opportunities for pharmacological intervention. This paper presents a novel approach using machine learning techniques to detect seizures onset using intracranial electroencephalography (EEG) signals. The proposed approach was tested on intracranial EEG data recorded in rats with pilocarpine model of temporal lobe epilepsy. A principal component analysis was applied for feature selection before using a support vector machine for the detection of seizures. Hjorth’s parameters and Daubechies discrete wavelet transform coefficients were found to be the most informative features of EEG data. We found that the support vector machine approach had a classification sensitivity of 90% and a specificity of 74% for detecting ictal episodes. Changing the epoch parameter from one to twenty-one seconds results in changing the redistribution of principal components’ values to 10% but does not affect the classification result. Support vector machines are accessible and convenient methods for classification that have achieved promising classification quality, and are rather lightweight compared to other machine learning methods. So we suggest their future use in mobile devices for early epileptic seizure and preictal episode detection. KW - Epilepsy KW - Single-channel intracranial encephalographic data KW - PCA KW - SVM KW - Automated system KW - Rats Y1 - 2025 U6 - https://doi.org/10.1109/ACCESS.2025.3527866 SN - 2169-3536 VL - 13 SP - 8951 EP - 8962 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway, NJ ER - TY - GEN A1 - Aftowicz, Marcin A1 - Fritscher, Markus A1 - Lehniger, Kai A1 - Wenger, Christian A1 - Langendörfer, Peter A1 - Brzozowski, Marcin T1 - Hardware-friendly Nyström approximation for water treatment anomaly detection T2 - IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society : proceedings N2 - This paper presents an approach to accelerate One-Class Support Vector Machines (SVM) using a hardware-friendly kernel that doesn't rely on multiplication operations, thus adaptable to hardware platforms. Leveraging Nyström approximation, we implemented a pipeline and compared its performance against a software implementation using libsvm. Furthermore, we evaluated the efficiency of our approach by deploying it on an FPGA. Our experiments, conducted on the SWaT dataset, demonstrate a 50x speedup using the FPGA implementation, achieving a classification time of 21 microseconds per instance. Importantly, we find no degradation in performance, as measured by the f-score of the attack class in the test set. This study explores the potential of hardware acceleration in optimizing anomaly detection systems for real-time applications. KW - OCSVM KW - FPGA KW - Nyström KW - Anomaly Detection KW - SWaT Y1 - 2024 SN - 978-1-6654-6454-3 U6 - https://doi.org/10.1109/IECON55916.2024.10905880 SP - 1 EP - 7 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway, NJ ER - TY - GEN A1 - Lehniger, Kai A1 - Langendorfer, Peter T1 - Elevating parallel shadow stack concepts for architectures with register windows T2 - IEEE transactions on dependable and secure computing N2 - Register windows are a processor feature that was originally developed to reduce the frequency for register spilling. This paper investigates how this mechanism can be used to implement a parallel shadow stack to protect return addresses from being corrupted. Furthermore, multiple improvements based on properties directly following the usage of register windows are proposed. Different configurations of parallel shadow stacks are evaluated based on prototype implementations on an ESP32 microcontroller and tested with different benchmarks. Performance overheads below 0.01% for CoreMark, below 3% for the worst case in MbedsTLS, and a worst case scenario of 22.153% for a recursive function show that our concepts are able to be efficiently developed even for embedded devices like the ESP32. KW - Buffer overflow KW - Shadow stack KW - Return-oriented programming KW - Register windows KW - Xtensa KW - ESP32 Y1 - 2025 U6 - https://doi.org/10.1109/TDSC.2025.3604556 SN - 1545-5971 SN - 1941-0018 SP - 1 EP - 18 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway, NJ ER -