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 -