@misc{AssafoLangendoerfer, author = {Assafo, Maryam and Langend{\"o}rfer, Peter}, title = {A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools under Different Operating Conditions}, series = {IEEE Access}, journal = {IEEE Access}, number = {9}, issn = {2169-3536}, doi = {10.1109/ACCESS.2021.3091476}, pages = {90011 -- 90028}, language = {en} } @misc{PetrykDykaPerezetal., author = {Petryk, Dmytro and Dyka, Zoya and P{\´e}rez, Eduardo and Kabin, Ievgen and Katzer, Jens and Sch{\"a}ffner, Jan and Langend{\"o}rfer, Peter}, title = {Sensitivity of HfO2-based RRAM Cells to Laser Irradiation}, series = {Microprocessors and Microsystems}, journal = {Microprocessors and Microsystems}, number = {87}, issn = {0141-9331}, doi = {10.1016/j.micpro.2021.104376}, language = {en} } @misc{VogelDykaKlannetal., author = {Vogel, Elisabeth and Dyka, Zoya and Klann, Dan and Langend{\"o}rfer, Peter}, title = {Resilience in the Cyber World: Definitions, Features and Models}, series = {Future Internet}, volume = {13}, journal = {Future Internet}, number = {11}, issn = {1999-5903}, doi = {10.3390/fi13110293}, language = {en} } @misc{KabinDykaKlannetal., author = {Kabin, Ievgen and Dyka, Zoya and Klann, Dan and Sch{\"a}ffner, Jan and Langend{\"o}rfer, Peter}, title = {On the Complexity of Attacking Commercial Authentication Products}, series = {Microprocessors and Microsystems}, journal = {Microprocessors and Microsystems}, number = {80}, issn = {0141-9331}, doi = {10.1016/j.micpro.2020.103480}, language = {en} } @misc{AssafoStaedterMeiseletal., author = {Assafo, Maryam and St{\"a}dter, Jost Philipp and Meisel, Tenia and Langend{\"o}rfer, Peter}, title = {On the Stability and Homogeneous Ensemble of Feature Selection for Predictive Maintenance: A Classification Application for Tool Condition Monitoring in Milling}, series = {Sensors}, volume = {23}, journal = {Sensors}, number = {9}, issn = {1424-8220}, doi = {10.3390/s23094461}, abstract = {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.}, language = {en} } @misc{AlsabbaghLangendoerfer, author = {Alsabbagh, Wael and Langend{\"o}rfer, Peter}, title = {A New Injection Threat on S7-1500 PLCs - Disrupting the Physical Process Offline}, series = {IEEE Open Journal of the Industrial Electronics Society}, journal = {IEEE Open Journal of the Industrial Electronics Society}, number = {3}, issn = {2644-1284}, doi = {10.1109/OJIES.2022.3151528}, pages = {146 -- 162}, language = {en} } @misc{KabinDykaLangendoerfer, author = {Kabin, Ievgen and Dyka, Zoya and Langend{\"o}rfer, Peter}, title = {Atomicity and Regularity Principles do not Ensure Full Resistance of ECC Designs against Single-Trace Attacks}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {8}, issn = {1424-8220}, doi = {10.3390/s22083083}, language = {en} } @misc{LehnigerSaadLangendoerfer, author = {Lehniger, Kai and Saad, Abdelaziz and Langend{\"o}rfer, Peter}, title = {Finding Gadgets in Incremental Code Updates for Return-Oriented Programming Attacks on Resource-Constrained Devices}, series = {Annals of Telecommunications}, volume = {78}, journal = {Annals of Telecommunications}, issn = {0003-4347}, doi = {10.1007/s12243-022-00917-8}, pages = {209 -- 229}, language = {en} } @misc{MartinLangendoerferZarrinetal., author = {Martin, Cristian and Langend{\"o}rfer, Peter and Zarrin, Pouya Soltani and D{\´i}az, Manuel and Rubio, Bartolom{\´e}}, title = {Kafka-ML: Connecting the Data Stream with ML/AI Frameworks}, series = {Future Generation Computer Systems}, volume = {126}, journal = {Future Generation Computer Systems}, issn = {0167-739X}, doi = {10.1016/j.future.2021.07.037}, pages = {15 -- 33}, language = {en} } @misc{EsfahaniMayeFrohbergetal., author = {Esfahani, Ali Haddadi and Maye, Oliver and Frohberg, Max and Speh, Maria and J{\"o}bges, Micheal and Langend{\"o}rfer, Peter}, title = {Real time detection of Freezing of Gait of Parkinson patients based on machine learning running on a body worn device}, series = {Procedia Computer Science}, volume = {239}, journal = {Procedia Computer Science}, publisher = {Elsevier BV}, issn = {1877-0509}, doi = {10.1016/j.procs.2024.06.160}, pages = {177 -- 184}, language = {en} } @misc{LehnigerLangendoerfer, author = {Lehniger, Kai and Langend{\"o}rfer, Peter}, title = {Comment on ''RIO: Return Instruction Obfuscation for Bare-Metal IoT Devices''}, series = {IEEE Access}, volume = {13}, journal = {IEEE Access}, publisher = {IEEE}, address = {Piscataway, NJ}, issn = {2169-3536}, doi = {10.1109/ACCESS.2025.3568598}, pages = {90358 -- 90361}, abstract = {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.}, language = {en} } @misc{PidvalnyiKostenkoSudakovetal., author = {Pidvalnyi, Illia and Kostenko, Anna and Sudakov, Oleksandr and Isaev, Dmytro and Maximyuk, Oleksandr and Krishtal, Oleg and Iegorova, Olena and Kabin, Ievgen and Dyka, Zoya and Ortmann, Steffen and Langend{\"o}rfer, Peter}, title = {Classification of epileptic seizures by simple machine learning techniques : application to animals' electroencephalography signals}, series = {IEEE access}, volume = {13}, journal = {IEEE access}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Piscataway, NJ}, issn = {2169-3536}, doi = {10.1109/ACCESS.2025.3527866}, pages = {8951 -- 8962}, abstract = {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.}, language = {en} }