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 -