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 Flashback on Control Logic Injection Attacks against Programmable Logic Controllers T2 - Automation N2 - Programmable logic controllers (PLCs) make up a substantial part of critical infrastructures (CIs) and industrial control systems (ICSs). They are programmed with a control logic that defines how to drive and operate critical processes such as nuclear power plants, petrochemical factories, water treatment systems, and other facilities. Unfortunately, these devices are not fully secure and are prone to malicious threats, especially those exploiting vulnerabilities in the control logic of PLCs. Such threats are known as control logic injection attacks. They mainly aim at sabotaging physical processes controlled by exposed PLCs, causing catastrophic damage to target systems as shown by Stuxnet. Looking back over the last decade, many research endeavors exploring and discussing these threats have been published. In this article, we present a flashback on the recent works related to control logic injection attacks against PLCs. To this end, we provide the security research community with a new systematization based on the attacker techniques under three main attack scenarios. For each study presented in this work, we overview the attack strategies, tools, security goals, infected devices, and underlying vulnerabilities. Based on our analysis, we highlight the current security challenges in protecting PLCs from such severe attacks and suggest security recommendations for future research directions. KW - industrial control system KW - programmable logic controller KW - control logic injection attack KW - program injection KW - program modification Y1 - 2022 U6 - https://doi.org/10.3390/automation3040030 SN - 2673-4052 VL - 3 IS - 4 SP - 596 EP - 621 ER - TY - GEN A1 - Natarov, Roman A1 - Dyka, Zoya A1 - Bohovyk, R. A1 - Fedoriuk, M. A1 - Isaev, Dmytro S. A1 - Sudakov, Oleksandr A1 - Maksymyuk, O. A1 - Krishtal, Oleg A. A1 - Langendörfer, Peter T1 - Artefacts in EEG Signals Epileptic Seizure Prediction using Edge Devices T2 - International Conference on Cyber-Physical Systems and Internet-of-Things (CPS & IoT 2020) Y1 - 2020 SN - 978-1-7281-6949-1 SN - 978-1-7281-6950-7 U6 - https://doi.org/10.1109/MECO49872.2020.9134076 ER - TY - GEN A1 - Mykytyn, Pavlo A1 - Kabin, Ievgen A1 - Dyka, Zoya A1 - Langendörfer, Peter T1 - Ensuring a Secure Communication Between a GCS and a UAV via the MAVlink Protocol T2 - Proc. 33rd Crypto-Day Matters 2021, (2021) Y1 - 2021 U6 - https://doi.org/10.18420/cdm-2021-33-21 ER - TY - GEN A1 - Assafo, Maryam A1 - Lautsch, Martin A1 - Suawa, Priscile Fogou A1 - Jongmanns, Marcel A1 - Hübner, Michael A1 - Reichenbach, Marc A1 - Brockmann, Carsten A1 - Reinhardt, Denis A1 - Langendörfer, Peter T1 - The ForTune Toolbox: Building Solutions for Condition-Based and Predictive Maintenance Focusing on Retrofitting Y1 - 2023 SN - 978-3-8007-6204-0 SN - 978-3-8007-6203-3 N1 - Poster, Tagungsband MikroSystemTechnik Kongress 2023, Dresden, 23. - 25. Oktober 2023 SP - S. 541 PB - VDE Verlag CY - Berlin ER - TY - GEN A1 - Mykytyn, Pavlo A1 - Brzozowski, Marcin A1 - Dyka, Zoya A1 - Langendörfer, Peter T1 - Jamming Detection for IR-UWB Ranging Technology in Autonomous UAV Swarms T2 - 2021 10th Mediterranean Conference on Embedded Computing (MECO) Y1 - 2021 SN - 978-1-6654-2989-4 SN - 978-1-6654-3912-1 U6 - https://doi.org/10.1109/MECO52532.2021.9460250 SN - 2637-9511 ER - TY - GEN A1 - Mykytyn, Pavlo A1 - Brzozowski, Marcin A1 - Dyka, Zoya A1 - Langendörfer, Peter T1 - GPS-Spoofing Attack Detection Mechanism for UAV Swarms T2 - 12th Mediterranean Conference on Embedded Computing (MECO) Y1 - 2023 SN - 979-8-3503-2290-3 SN - 979-8-3503-2291-0 U6 - https://doi.org/10.1109/MECO58584.2023.10154998 SN - 2637-9511 ER -