Publications 2025
Script list Publications
(1) Hardware-Friendly Nyström Approximation for Water Treatment Anomaly Detection
M. Aftowicz, M. Fritscher, K. Lehniger, Ch. Wenger, P. Langendörfer, M. Brzozowski
Proc. 50th IEEE Industrial Electronics Society (IECON 2024), (2025)
DOI: 10.1109/IECON55916.2024.10905880, (KISS-KI)
(2) A Payload of Lies: False Data Injection Attacks on MQTT-based IIoT Systems
W. Alsabbagh, C. Kim, P. Langendörfer
Proc. 50th Annual Conference of the IEEE Industrial Electronics Society (IECON 2024), (2025)
DOI: 10.1109/IECON55916.2024.10905487
(3) Impact of Thermal Effects on Cryptographic Resilience: A Study of an ASIC Implementation of the Montgomery Ladder
I. Kabin, P. Langendörfer, Z. Dyka
Proc. 26th IEEE Latin American Test Symposium (LATS 2025), (2025)
DOI: 10.1109/LATS65346.2025.10963949, (Resilient Systems)
(4) Impact of Thermal Effects on Cryptographic Resilience: A Study of an ASIC Implementation of the Montgomery Ladder
I. Kabin, P. Langendörfer, Z. Dyka
Proc. 26th IEEE Latin American Test Symposium (LATS 2025), (2025)
DOI: 10.1109/LATS65346.2025.10963949, (Total Resilience)
(5) Compatibility Check of SmartDSM with the IDSA Reference Architecture
I. Koropiecki, K. Piotrowski
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024)
(SmartRiver)
(6) Compatibility Check of SmartDSM with the IDSA Reference Architecture
I. Koropiecki, K. Piotrowski
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024)
(ebalance plus)
(7) Modular Data-Centric Tool for Wireless Sensor Network Monitoring and Managing
J. Maj, P. Zielony, K. Piotrowski
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024), 40 (2025)
(8) A 434 MHz Low-Power Receiver System Based on a Switched Passive Input Network with Surface Acoustic Wave Resonator
G. Meller, M. Methfessel, F. Protze, J. Wagner, F. Ellinger
Proc. 16th German Microwave Conference (GeMiC 2025), 183 (2025)
DOI: 10.23919/GeMiC64734.2025.10979051, (WakeMeUp)
(9) Vulnerable or Not: SCA Test Results Strongly Depend on the Measurement Equipment
D. Petryk, I. Kabin, Z. Dyka
Proc. 37. ITG/GMM/GI-Workshop Testmethoden und Zuverlässigkeit von Schaltungen und Systemen (TuZ 2025), (2025)
(Resilient Systems)
(10) Horizontal Side-Channel Analysis Attack against Elliptic Curve Scalar Multiplication Accelerator under Laser Illumination
D. Petryk, I. Kabin, P. Langendörfer, Z. Dyka
Proc. 26th IEEE Latin American Test Symposium (LATS 2025), (2025)
DOI: 10.1109/LATS65346.2025.10963958, (Resilient Systems)
(11) Vulnerable or Not: SCA Test Results Strongly Depend on the Measurement Equipment
D. Petryk, I. Kabin, Z. Dyka
Proc. 37. ITG/GMM/GI-Workshop Testmethoden und Zuverlässigkeit von Schaltungen und Systemen (TuZ 2025), (2025)
(Total Resilience)
(12) Horizontal Side-Channel Analysis Attack against Elliptic Curve Scalar Multiplication Accelerator under Laser Illumination
D. Petryk, I. Kabin, P. Langendörfer, Z. Dyka
Proc. 26th IEEE Latin American Test Symposium (LATS 2025), (2025)
DOI: 10.1109/LATS65346.2025.10963958, (Total Resilience)
(13) SCA Test Results Depend on the Measurement Equipment: Riscure vs. Teledyne LeCroy
D. Petryk, Z. Dyka, P. Langendörfer, I. Kabin
Proc. 3rd Workshop on Nano Security: From Nano-Electronics to Secure Systems (NanoSec 2025), (2025)
(Resilient Systems)
(14) SCA Test Results Depend on the Measurement Equipment: Riscure vs. Teledyne LeCroy
D. Petryk, Z. Dyka, P. Langendörfer, I. Kabin
Proc. 3rd Workshop on Nano Security: From Nano-Electronics to Secure Systems (NanoSec 2025), (2025)
(Total Resilience)
(15) Classification of Epileptic Seizures by Simple Machine Learning Techniques: Application to Animals’ Electroencephalography Signals
I. Pidvalnyi, A. Kostenko, O. Sudakov, D. Isaev, O. Maximyuk, O. Krishtal, O. Iegorova, I. Kabin, Z. Dyka, S. Ortmann, P. Langendörfer
IEEE Access 13, 8951 (2025)
DOI: 10.1109/ACCESS.2025.3527866, (Total Resilience)
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 electroencephalography (EEG) signals. The proposed approach was tested on 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 episodes detection.
(16) Classification of Epileptic Seizures by Simple Machine Learning Techniques: Application to Animals’ Electroencephalography Signals
I. Pidvalnyi, A. Kostenko, O. Sudakov, D. Isaev, O. Maximyuk, O. Krishtal, O. Iegorova, I. Kabin, Z. Dyka, S. Ortmann, P. Langendörfer
IEEE Access 13, 8951 (2025)
DOI: 10.1109/ACCESS.2025.3527866, (DFG-Resilient Systems for Real Time Prediction of Epileptic Seizures)
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 electroencephalography (EEG) signals. The proposed approach was tested on 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 episodes detection.
(17) Resistance Test Discovered an Inherent Vulnerability of Cryptographic ASICs to Simple SCA
A.A. Sigourou, Z. Dyka, I. Kabin
Proc. 37. ITG/GMM/GI-Workshop Testmethoden und Zuverlässigkeit von Schaltungen und Systemen (TuZ 2025), (2025)
(Resilient Systems)
(18) Resistance Test Discovered an Inherent Vulnerability of Cryptographic ASICs to Simple SCA
A.A. Sigourou, Z. Dyka, I. Kabin
Proc. 37. ITG/GMM/GI-Workshop Testmethoden und Zuverlässigkeit von Schaltungen und Systemen (TuZ 2025), (2025)
(Total Resilience)
(19) Distinguishability between Multiplication and Squaring Operations: A New Marker
A.A. Sigourou, Z. Dyka, P. Langendörfer, I. Kabin
Proc. 3rd Workshop on Nano Security: From Nano-Electronics to Secure Systems (NanoSec 2025), (2025)
(Total Resilience)
(20) Distinguishability between Multiplication and Squaring Operations: A New Marker
A.A. Sigourou, Z. Dyka, P. Langendörfer, I. Kabin
Proc. 3rd Workshop on Nano Security: From Nano-Electronics to Secure Systems (NanoSec 2025), (2025)
(Resilient Systems)
(21) Analysis of Frameworks for Developing Artificial Intelligence Models on Low-Power Microcontrollers in Wireless Sensor Networks: Capabilities and Challenges
K. Turchan, K. Piotrowski
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024), 21 (2025)
(22) Challenges in Developing Modular AI Applications
K. Woloszyn, K. Piotrowski, K. Turchan
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024), 18 (2025)
(23) Pushing the Limits of 5G Private Networks: A Practical Examination of Network Stressors
O. Yener, M. Brzozowski, R. Chitauro, P. Langendörfer
Proc. International Conference on Computing, Networking and Communications (ICNC 2025), 752 (2025)
DOI: 10.1109/ICNC64010.2025.10993913, (EMiL)
(24) Pushing the Limits of 5G Private Networks: A Practical Examination of Network Stressors
O. Yener, M. Brzozowski, R. Chitauro, P. Langendörfer
Proc. International Conference on Computing, Networking and Communications (ICNC 2025), 752 (2025)
DOI: 10.1109/ICNC64010.2025.10993913, (Open 6G Hub)
(25) Pushing the Limits of 5G Private Networks: A Practical Examination of Network Stressors
O. Yener, M. Brzozowski, R. Chitauro, P. Langendörfer
Proc. International Conference on Computing, Networking and Communications (ICNC 2025), 752 (2025)
DOI: 10.1109/ICNC64010.2025.10993913, (iCampus II)
(26) tinyDSM-Based Modular Approach in Facilitating IoT Application Development and Maintenance
P. Zielony, K. Piotrowski
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024), 8 (2025)
M. Aftowicz, M. Fritscher, K. Lehniger, Ch. Wenger, P. Langendörfer, M. Brzozowski
Proc. 50th IEEE Industrial Electronics Society (IECON 2024), (2025)
DOI: 10.1109/IECON55916.2024.10905880, (KISS-KI)
(2) A Payload of Lies: False Data Injection Attacks on MQTT-based IIoT Systems
W. Alsabbagh, C. Kim, P. Langendörfer
Proc. 50th Annual Conference of the IEEE Industrial Electronics Society (IECON 2024), (2025)
DOI: 10.1109/IECON55916.2024.10905487
(3) Impact of Thermal Effects on Cryptographic Resilience: A Study of an ASIC Implementation of the Montgomery Ladder
I. Kabin, P. Langendörfer, Z. Dyka
Proc. 26th IEEE Latin American Test Symposium (LATS 2025), (2025)
DOI: 10.1109/LATS65346.2025.10963949, (Resilient Systems)
(4) Impact of Thermal Effects on Cryptographic Resilience: A Study of an ASIC Implementation of the Montgomery Ladder
I. Kabin, P. Langendörfer, Z. Dyka
Proc. 26th IEEE Latin American Test Symposium (LATS 2025), (2025)
DOI: 10.1109/LATS65346.2025.10963949, (Total Resilience)
(5) Compatibility Check of SmartDSM with the IDSA Reference Architecture
I. Koropiecki, K. Piotrowski
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024)
(SmartRiver)
(6) Compatibility Check of SmartDSM with the IDSA Reference Architecture
I. Koropiecki, K. Piotrowski
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024)
(ebalance plus)
(7) Modular Data-Centric Tool for Wireless Sensor Network Monitoring and Managing
J. Maj, P. Zielony, K. Piotrowski
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024), 40 (2025)
(8) A 434 MHz Low-Power Receiver System Based on a Switched Passive Input Network with Surface Acoustic Wave Resonator
G. Meller, M. Methfessel, F. Protze, J. Wagner, F. Ellinger
Proc. 16th German Microwave Conference (GeMiC 2025), 183 (2025)
DOI: 10.23919/GeMiC64734.2025.10979051, (WakeMeUp)
(9) Vulnerable or Not: SCA Test Results Strongly Depend on the Measurement Equipment
D. Petryk, I. Kabin, Z. Dyka
Proc. 37. ITG/GMM/GI-Workshop Testmethoden und Zuverlässigkeit von Schaltungen und Systemen (TuZ 2025), (2025)
(Resilient Systems)
(10) Horizontal Side-Channel Analysis Attack against Elliptic Curve Scalar Multiplication Accelerator under Laser Illumination
D. Petryk, I. Kabin, P. Langendörfer, Z. Dyka
Proc. 26th IEEE Latin American Test Symposium (LATS 2025), (2025)
DOI: 10.1109/LATS65346.2025.10963958, (Resilient Systems)
(11) Vulnerable or Not: SCA Test Results Strongly Depend on the Measurement Equipment
D. Petryk, I. Kabin, Z. Dyka
Proc. 37. ITG/GMM/GI-Workshop Testmethoden und Zuverlässigkeit von Schaltungen und Systemen (TuZ 2025), (2025)
(Total Resilience)
(12) Horizontal Side-Channel Analysis Attack against Elliptic Curve Scalar Multiplication Accelerator under Laser Illumination
D. Petryk, I. Kabin, P. Langendörfer, Z. Dyka
Proc. 26th IEEE Latin American Test Symposium (LATS 2025), (2025)
DOI: 10.1109/LATS65346.2025.10963958, (Total Resilience)
(13) SCA Test Results Depend on the Measurement Equipment: Riscure vs. Teledyne LeCroy
D. Petryk, Z. Dyka, P. Langendörfer, I. Kabin
Proc. 3rd Workshop on Nano Security: From Nano-Electronics to Secure Systems (NanoSec 2025), (2025)
(Resilient Systems)
(14) SCA Test Results Depend on the Measurement Equipment: Riscure vs. Teledyne LeCroy
D. Petryk, Z. Dyka, P. Langendörfer, I. Kabin
Proc. 3rd Workshop on Nano Security: From Nano-Electronics to Secure Systems (NanoSec 2025), (2025)
(Total Resilience)
(15) Classification of Epileptic Seizures by Simple Machine Learning Techniques: Application to Animals’ Electroencephalography Signals
I. Pidvalnyi, A. Kostenko, O. Sudakov, D. Isaev, O. Maximyuk, O. Krishtal, O. Iegorova, I. Kabin, Z. Dyka, S. Ortmann, P. Langendörfer
IEEE Access 13, 8951 (2025)
DOI: 10.1109/ACCESS.2025.3527866, (Total Resilience)
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 electroencephalography (EEG) signals. The proposed approach was tested on 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 episodes detection.
(16) Classification of Epileptic Seizures by Simple Machine Learning Techniques: Application to Animals’ Electroencephalography Signals
I. Pidvalnyi, A. Kostenko, O. Sudakov, D. Isaev, O. Maximyuk, O. Krishtal, O. Iegorova, I. Kabin, Z. Dyka, S. Ortmann, P. Langendörfer
IEEE Access 13, 8951 (2025)
DOI: 10.1109/ACCESS.2025.3527866, (DFG-Resilient Systems for Real Time Prediction of Epileptic Seizures)
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 electroencephalography (EEG) signals. The proposed approach was tested on 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 episodes detection.
(17) Resistance Test Discovered an Inherent Vulnerability of Cryptographic ASICs to Simple SCA
A.A. Sigourou, Z. Dyka, I. Kabin
Proc. 37. ITG/GMM/GI-Workshop Testmethoden und Zuverlässigkeit von Schaltungen und Systemen (TuZ 2025), (2025)
(Resilient Systems)
(18) Resistance Test Discovered an Inherent Vulnerability of Cryptographic ASICs to Simple SCA
A.A. Sigourou, Z. Dyka, I. Kabin
Proc. 37. ITG/GMM/GI-Workshop Testmethoden und Zuverlässigkeit von Schaltungen und Systemen (TuZ 2025), (2025)
(Total Resilience)
(19) Distinguishability between Multiplication and Squaring Operations: A New Marker
A.A. Sigourou, Z. Dyka, P. Langendörfer, I. Kabin
Proc. 3rd Workshop on Nano Security: From Nano-Electronics to Secure Systems (NanoSec 2025), (2025)
(Total Resilience)
(20) Distinguishability between Multiplication and Squaring Operations: A New Marker
A.A. Sigourou, Z. Dyka, P. Langendörfer, I. Kabin
Proc. 3rd Workshop on Nano Security: From Nano-Electronics to Secure Systems (NanoSec 2025), (2025)
(Resilient Systems)
(21) Analysis of Frameworks for Developing Artificial Intelligence Models on Low-Power Microcontrollers in Wireless Sensor Networks: Capabilities and Challenges
K. Turchan, K. Piotrowski
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024), 21 (2025)
(22) Challenges in Developing Modular AI Applications
K. Woloszyn, K. Piotrowski, K. Turchan
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024), 18 (2025)
(23) Pushing the Limits of 5G Private Networks: A Practical Examination of Network Stressors
O. Yener, M. Brzozowski, R. Chitauro, P. Langendörfer
Proc. International Conference on Computing, Networking and Communications (ICNC 2025), 752 (2025)
DOI: 10.1109/ICNC64010.2025.10993913, (EMiL)
(24) Pushing the Limits of 5G Private Networks: A Practical Examination of Network Stressors
O. Yener, M. Brzozowski, R. Chitauro, P. Langendörfer
Proc. International Conference on Computing, Networking and Communications (ICNC 2025), 752 (2025)
DOI: 10.1109/ICNC64010.2025.10993913, (Open 6G Hub)
(25) Pushing the Limits of 5G Private Networks: A Practical Examination of Network Stressors
O. Yener, M. Brzozowski, R. Chitauro, P. Langendörfer
Proc. International Conference on Computing, Networking and Communications (ICNC 2025), 752 (2025)
DOI: 10.1109/ICNC64010.2025.10993913, (iCampus II)
(26) tinyDSM-Based Modular Approach in Facilitating IoT Application Development and Maintenance
P. Zielony, K. Piotrowski
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024), 8 (2025)
