Script list Publications
(1) A Stealthy False Command Injection Attack on Modbus based SCADA Systems
W. Alsabbagh, S. Amogbonjaye, D. Urrego and P. Langendörfer
Proc. 5th International Workshop on Security Trust Privacy for Cyber-Physical Systems (STP-CPS 2023), (2023)
DOI: 10.1109/CCNC51644.2023.10059804, (KITS)
(2) On the Stability and Homogeneous Ensemble of Feature Selection for Predictive Maintenance: A Classification Application for Tool Condition Monitoring in Milling
M. Assafo, J.P. Städter, T. Meisel, P. Langendörfer
Sensors (MDPI) 23(9), 4461 (2023)
DOI: 10.3390/s23094461, (iCampus)
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.
(3) Information Security: The Cornerstone for Surviving the Digital Wild
P. Langendörfer, St. Kornemann, W. Alsabbagh, E. Hermann
The Future of Smart Production for SMEs, 1st Edition, Editors: O. Madsen, U. Berger, C. Moller, A. Lassen Heidemann, B. Waehrens Vejrum, C. Schou, Chapter. Information Security: The Cornerstone for Surviving the Digital Wild, Springer, 335 (2023)
DOI: 10.1007/978-3-031-15428-7_29, (KITS)
(4) On the Feasibility of Single-Trace Attacks on the Gaussian Sampler Used a CDT
S. Marzougui, I. Kabin, J. Krämer, T. Aulbach, J.-P. Seifert
Proc. International Workshop on Constructive Side-Channel Analysis and Secure Design (COSADE 2023), in: Lecture Notes in Computer Science, Springer, LNCS 13979, 149 (2023)
DOI: 10.1007/978-3-031-29497-6_8, (Total Resilience)
W. Alsabbagh, S. Amogbonjaye, D. Urrego and P. Langendörfer
Proc. 5th International Workshop on Security Trust Privacy for Cyber-Physical Systems (STP-CPS 2023), (2023)
DOI: 10.1109/CCNC51644.2023.10059804, (KITS)
(2) On the Stability and Homogeneous Ensemble of Feature Selection for Predictive Maintenance: A Classification Application for Tool Condition Monitoring in Milling
M. Assafo, J.P. Städter, T. Meisel, P. Langendörfer
Sensors (MDPI) 23(9), 4461 (2023)
DOI: 10.3390/s23094461, (iCampus)
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.
(3) Information Security: The Cornerstone for Surviving the Digital Wild
P. Langendörfer, St. Kornemann, W. Alsabbagh, E. Hermann
The Future of Smart Production for SMEs, 1st Edition, Editors: O. Madsen, U. Berger, C. Moller, A. Lassen Heidemann, B. Waehrens Vejrum, C. Schou, Chapter. Information Security: The Cornerstone for Surviving the Digital Wild, Springer, 335 (2023)
DOI: 10.1007/978-3-031-15428-7_29, (KITS)
(4) On the Feasibility of Single-Trace Attacks on the Gaussian Sampler Used a CDT
S. Marzougui, I. Kabin, J. Krämer, T. Aulbach, J.-P. Seifert
Proc. International Workshop on Constructive Side-Channel Analysis and Secure Design (COSADE 2023), in: Lecture Notes in Computer Science, Springer, LNCS 13979, 149 (2023)
DOI: 10.1007/978-3-031-29497-6_8, (Total Resilience)
