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) Smart Traps for Smart Systems: Scalable Honeynets for IIoT Cybersecurity
W. Alsabbagh, D. Urrego, P. Langendörfer
Proc. 34th International Conference on Computer Communication and Networks (ICCCN 2025), (2025)
DOI: 10.1109/ICCCN65249.2025.11133761
(4) FeatNet-IDS: Anomaly Detection based-Features for Industrial Internet of Things Systems
W. Alsabbagh, B. Sayegh, C. Kim, P. Langendörfer
Proc. 6th Silicon Valley Cybersecurity Conference (SVCC 2025), (2025)
DOI: 10.1109/ISVLSI65124.2025.11130195
(5) Self-Organizing Map Applications for Predictive Maintenance: A Review
M. Assafo, P. Langendörfer
Proc. 8th IEEE Conference on Industrial Cyber-Physical Systems (ICPS 2025), (2025)
DOI: 10.1109/ICPS65515.2025.11087894, (D4M)
(6) Tool Remaining Useful Life Prediction using Feature Extraction and Machine Learning-based Sensor Fusion
M. Assafo, P. Langendörfer
Results in Engineering 28, 107297 (2025)
DOI: 10.1016/j.rineng.2025.107297
Tool remaining useful life prediction (RUL) is a critical task for predictive maintenance in manufacturing. Common limitations of existing data-driven solutions include: 1) Dependence on tool wear labels which are intricate to obtain on shop floors. 2) High resource requirements, affecting applicability on resource-constrained Internet-of-things devices. 3) Heavy feature engineering. To address these limitations, we present a methodology aiming at accurately predicting RUL without using wear labels, while ensuring implementation efficiency and minimal feature engineering. It involves extracting time-domain features and multiscale features using maximal overlap discrete wavelet transform (MODWT) from three cutting-force sensor signals. Without undergoing any feature selection or dimensionality reduction, the features are fed to machine learning (ML) regression models where they are fused into an RUL decision. For this purpose, one-to-one and sequence-to-sequence regression using random forest (RF) and different long short-term memory (LSTM) networks were used, respectively. The 2010 PHM Data Challenge milling dataset was used for validation. The results highlighted the significant role of sensor fusion in reducing prediction errors and increasing the performance consistency over three test cutters, compared to single sensors. Global interpretations were provided using RF-based feature importance analysis. Our methodology was compared with six existing state-of-the-art works, including different end-to-end deep learning (DL) models using raw data as input, and works coupling heavy feature engineering with DL. The results showed that our methodology consistently outperformed all the comparative methods over the test cutters, despite using fewer sensors, which further proves its competitiveness and suitability in resource- and sensor-constrained environments.
(7) 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)
(8) On the SCA Resistance of TMR-Protected Cryptographic Designs
I. Kabin, P. Langendörfer, Z. Dyka
Electronics (MDPI) 14(16), 3318 (2025)
DOI: 10.3390/electronics14163318, (Resilient Systems)
The influence of redundant implementations on success of physical attacks against cryptographic devices is currently underresearched. This is especially an issue in application fields such as wearable health, industrial control systems and the like in which devices are accessible to potential attackers. This paper presents results of an investigation of the TMR application impact on the vulnerability of FPGA-based asymmetric cryptographic accelerators to side-channel analysis attacks. We implemented our cryptographic cores using full- and partial-TMR application approaches and experimentally conducted evaluation of their side-channel resistance. Our results reveal that TMR can significantly impact side-channel leakage, either increasing resistance by introducing noise or amplifying leakage depending on the part of the design where redundancy was applied.
(9) 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)
(10) Compatibility Check of SmartDSM with the IDSA Reference Architecture
I. Koropiecki, K. Piotrowski
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024)
(ebalance plus)
(11) Compatibility Check of SmartDSM with the IDSA Reference Architecture
I. Koropiecki, K. Piotrowski
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024)
(SmartRiver)
(12) Comments on: RIO: Return Instruction Obfuscation for Bare-Metal IoT Devices
K. Lehniger, P. Langendörfer
IEEE Access 13, 90358 (2025)
DOI: 10.1109/ACCESS.2025.3568598
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.
(13) Investigating Compact Shadow Stacks for the Xtensa LX Architecture
K. Lehniger, P. Langendörfer
Proc. 14th Mediterranean Conference on Embedded Computing (MECO 2025), (2025)
DOI: 10.1109/MECO66322.2025.11049161
(14) 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)
(15) An Analog Frontend for an Ultra Low Power Wakeup Receiver for On-Off Keying at 434 MHz with -94 dBm Input Sensitivity and 28 nW DC Power Consumption at 10 kbps
G. Meller, M. Methfessel, F. Protze, M. Froitzheim, J. Wagner, G. Fischer, F. Ellinger
Proc. 28th European Microwave Week (EuMW 2025), 142 (2025)
(WakeMeUp)
(16) 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)
(17) Channel State Information Analysis for Jamming Attack Detection in Static and Dynamic UAV Networks – An Experimental Study
P. Mykytyn, R. Chitauro, Z. Dyka, P. Langendörfer
Proc. 21st Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2025), 322 (2025)
DOI: 10.1109/DCOSS-IoT65416.2025.00060, (EMiL)
(18) Channel State Information Analysis for Jamming Attack Detection in Static and Dynamic UAV Networks – An Experimental Study
P. Mykytyn, R. Chitauro, Z. Dyka, P. Langendörfer
Proc. 21st Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2025), 322 (2025)
DOI: 10.1109/DCOSS-IoT65416.2025.00060, (iCampus II)
(19) 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)
(20) Sensitivity of Logic Cells to Laser Fault Injections: An Overview of Experimental Results for IHP Technologies
D. Petryk, P. Langendörfer, Z. Dyka
IEEE Transactions on Device and Materials Reliability 25(3), 410 (2025)
DOI: 10.1109/TDMR.2025.3596380, (Resilient Systems)
In this work, we provide an overview of our front-side Fault Injection (FI) experiments with different logic cells manufactured in two IHP BiCMOS technologies using Riscure equipment for laser FIs. We were able to inject faults into different types of cells including standard library cells as well as into two types of radiation tolerant flip-flops. Experimenting with radiation-tolerant flip-flops faults were injected illuminating areas with PMOS transistors in OFF state. We determined the cells areas, which were sensitive to the laser FI attacks. Only few works discussed this aspect in the past determining NMOS transistors as the sensitive part of the logic cells. Knowledge about the areas which are sensitive to the laser FI attacks can be generalized experimenting with other technologies and used in future by designers to implement corresponding countermeasure(s) at the initial stage of chip development.
(21) Horizontal Attack against EC kP Accelerator under Laser Illumination
D. Petryk, I. Kabin, P. Langendörfer, Z. Dyka
Electronics (MDPI) 14(10), 2072 (2025)
DOI: 10.3390/electronics14102072, (Total Resilience)
Devices employing cryptographic approaches have to be resistant to physical attacks. Side-Channel Analysis (SCA) and Fault Injection (FI) attacks are frequently used to reveal cryptographic keys. In this paper, we present a combined SCA and laser illumination attack against an Elliptic Curve Scalar Multiplication accelerator, while using different equipment for the measurement of its power traces, i.e., we performed the measurements using a current probe from Riscure and a differential probe from Teledyne LeCroy, with an attack success of 70% and 90%, respectively. Our experiments showed that laser illumination increased the power consumption of the chip, especially its static power consumption, but the success of the horizontal power analysis attacks changed insignificantly. After applying 100% of the laser beam output power and illuminating the smallest area of 143 µm2, we observed an offset of 17 mV in the measured trace. We assume that using a laser with a high laser beam power, as well as concentrating on measuring and analysing only static current, can significantly improve the attack’s success. The attacks exploiting the Static Current under Laser Illumination (SCuLI attacks) are novel, and their potential has not yet been fully investigated. These attacks can be especially dangerous against cryptographic chips manufactured in downscaling technologies. If such attacks are feasible, appropriate countermeasures have to be proposed in the future.
(22) Horizontal Attack against EC kP Accelerator under Laser Illumination
D. Petryk, I. Kabin, P. Langendörfer, Z. Dyka
Electronics (MDPI) 14(10), 2072 (2025)
DOI: 10.3390/electronics14102072, (Resilient Systems)
Devices employing cryptographic approaches have to be resistant to physical attacks. Side-Channel Analysis (SCA) and Fault Injection (FI) attacks are frequently used to reveal cryptographic keys. In this paper, we present a combined SCA and laser illumination attack against an Elliptic Curve Scalar Multiplication accelerator, while using different equipment for the measurement of its power traces, i.e., we performed the measurements using a current probe from Riscure and a differential probe from Teledyne LeCroy, with an attack success of 70% and 90%, respectively. Our experiments showed that laser illumination increased the power consumption of the chip, especially its static power consumption, but the success of the horizontal power analysis attacks changed insignificantly. After applying 100% of the laser beam output power and illuminating the smallest area of 143 µm2, we observed an offset of 17 mV in the measured trace. We assume that using a laser with a high laser beam power, as well as concentrating on measuring and analysing only static current, can significantly improve the attack’s success. The attacks exploiting the Static Current under Laser Illumination (SCuLI attacks) are novel, and their potential has not yet been fully investigated. These attacks can be especially dangerous against cryptographic chips manufactured in downscaling technologies. If such attacks are feasible, appropriate countermeasures have to be proposed in the future.
(23) 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)
(24) 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)
(25) 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)
(26) 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)
(27) 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)
(28) 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.
(29) 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.
(30) Atomic Patterns: Field Operation Distinguishability on Cryptographic ASICs
A.A. Sigourou, Z. Dyka, P. Langendörfer, I. Kabin
Proc. IEEE International Conference on Cyber Security and Resilience (CSR 2025), 990 (2025)
DOI: 10.1109/CSR64739.2025.11130154, (Total Resilience)
(31) Atomic Patterns: Field Operation Distinguishability on Cryptographic ASICs
A.A. Sigourou, Z. Dyka, P. Langendörfer, I. Kabin
Proc. IEEE International Conference on Cyber Security and Resilience (CSR 2025), 990 (2025)
DOI: 10.1109/CSR64739.2025.11130154, (Resilient Systems)
(32) Revisiting Atomic Patterns for Elliptic Curve Scalar Multiplication Revealing Inherent Vulnerability to Simple SCA
A.A. Sigourou, Z. Dyka, S.H. Li , P. Langendörfer, I. Kabin
Proc. 12th IFIP International Conference on New Technologies, Mobility and Security (NTMS 2025), 252 (2025)
DOI: 10.1109/NTMS65597.2025.11076762, (Total Resilience)
(33) 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)
(34) 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)
(35) 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)
(36) 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)
(37) 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)
(38) Challenges in Developing Modular AI Applications
K. Woloszyn, K. Piotrowski, K. Turchan
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024), 18 (2025)
(39) 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)
(40) 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)
(41) 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)
(42) 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) Smart Traps for Smart Systems: Scalable Honeynets for IIoT Cybersecurity
W. Alsabbagh, D. Urrego, P. Langendörfer
Proc. 34th International Conference on Computer Communication and Networks (ICCCN 2025), (2025)
DOI: 10.1109/ICCCN65249.2025.11133761
(4) FeatNet-IDS: Anomaly Detection based-Features for Industrial Internet of Things Systems
W. Alsabbagh, B. Sayegh, C. Kim, P. Langendörfer
Proc. 6th Silicon Valley Cybersecurity Conference (SVCC 2025), (2025)
DOI: 10.1109/ISVLSI65124.2025.11130195
(5) Self-Organizing Map Applications for Predictive Maintenance: A Review
M. Assafo, P. Langendörfer
Proc. 8th IEEE Conference on Industrial Cyber-Physical Systems (ICPS 2025), (2025)
DOI: 10.1109/ICPS65515.2025.11087894, (D4M)
(6) Tool Remaining Useful Life Prediction using Feature Extraction and Machine Learning-based Sensor Fusion
M. Assafo, P. Langendörfer
Results in Engineering 28, 107297 (2025)
DOI: 10.1016/j.rineng.2025.107297
Tool remaining useful life prediction (RUL) is a critical task for predictive maintenance in manufacturing. Common limitations of existing data-driven solutions include: 1) Dependence on tool wear labels which are intricate to obtain on shop floors. 2) High resource requirements, affecting applicability on resource-constrained Internet-of-things devices. 3) Heavy feature engineering. To address these limitations, we present a methodology aiming at accurately predicting RUL without using wear labels, while ensuring implementation efficiency and minimal feature engineering. It involves extracting time-domain features and multiscale features using maximal overlap discrete wavelet transform (MODWT) from three cutting-force sensor signals. Without undergoing any feature selection or dimensionality reduction, the features are fed to machine learning (ML) regression models where they are fused into an RUL decision. For this purpose, one-to-one and sequence-to-sequence regression using random forest (RF) and different long short-term memory (LSTM) networks were used, respectively. The 2010 PHM Data Challenge milling dataset was used for validation. The results highlighted the significant role of sensor fusion in reducing prediction errors and increasing the performance consistency over three test cutters, compared to single sensors. Global interpretations were provided using RF-based feature importance analysis. Our methodology was compared with six existing state-of-the-art works, including different end-to-end deep learning (DL) models using raw data as input, and works coupling heavy feature engineering with DL. The results showed that our methodology consistently outperformed all the comparative methods over the test cutters, despite using fewer sensors, which further proves its competitiveness and suitability in resource- and sensor-constrained environments.
(7) 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)
(8) On the SCA Resistance of TMR-Protected Cryptographic Designs
I. Kabin, P. Langendörfer, Z. Dyka
Electronics (MDPI) 14(16), 3318 (2025)
DOI: 10.3390/electronics14163318, (Resilient Systems)
The influence of redundant implementations on success of physical attacks against cryptographic devices is currently underresearched. This is especially an issue in application fields such as wearable health, industrial control systems and the like in which devices are accessible to potential attackers. This paper presents results of an investigation of the TMR application impact on the vulnerability of FPGA-based asymmetric cryptographic accelerators to side-channel analysis attacks. We implemented our cryptographic cores using full- and partial-TMR application approaches and experimentally conducted evaluation of their side-channel resistance. Our results reveal that TMR can significantly impact side-channel leakage, either increasing resistance by introducing noise or amplifying leakage depending on the part of the design where redundancy was applied.
(9) 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)
(10) Compatibility Check of SmartDSM with the IDSA Reference Architecture
I. Koropiecki, K. Piotrowski
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024)
(ebalance plus)
(11) Compatibility Check of SmartDSM with the IDSA Reference Architecture
I. Koropiecki, K. Piotrowski
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024)
(SmartRiver)
(12) Comments on: RIO: Return Instruction Obfuscation for Bare-Metal IoT Devices
K. Lehniger, P. Langendörfer
IEEE Access 13, 90358 (2025)
DOI: 10.1109/ACCESS.2025.3568598
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.
(13) Investigating Compact Shadow Stacks for the Xtensa LX Architecture
K. Lehniger, P. Langendörfer
Proc. 14th Mediterranean Conference on Embedded Computing (MECO 2025), (2025)
DOI: 10.1109/MECO66322.2025.11049161
(14) 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)
(15) An Analog Frontend for an Ultra Low Power Wakeup Receiver for On-Off Keying at 434 MHz with -94 dBm Input Sensitivity and 28 nW DC Power Consumption at 10 kbps
G. Meller, M. Methfessel, F. Protze, M. Froitzheim, J. Wagner, G. Fischer, F. Ellinger
Proc. 28th European Microwave Week (EuMW 2025), 142 (2025)
(WakeMeUp)
(16) 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)
(17) Channel State Information Analysis for Jamming Attack Detection in Static and Dynamic UAV Networks – An Experimental Study
P. Mykytyn, R. Chitauro, Z. Dyka, P. Langendörfer
Proc. 21st Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2025), 322 (2025)
DOI: 10.1109/DCOSS-IoT65416.2025.00060, (EMiL)
(18) Channel State Information Analysis for Jamming Attack Detection in Static and Dynamic UAV Networks – An Experimental Study
P. Mykytyn, R. Chitauro, Z. Dyka, P. Langendörfer
Proc. 21st Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2025), 322 (2025)
DOI: 10.1109/DCOSS-IoT65416.2025.00060, (iCampus II)
(19) 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)
(20) Sensitivity of Logic Cells to Laser Fault Injections: An Overview of Experimental Results for IHP Technologies
D. Petryk, P. Langendörfer, Z. Dyka
IEEE Transactions on Device and Materials Reliability 25(3), 410 (2025)
DOI: 10.1109/TDMR.2025.3596380, (Resilient Systems)
In this work, we provide an overview of our front-side Fault Injection (FI) experiments with different logic cells manufactured in two IHP BiCMOS technologies using Riscure equipment for laser FIs. We were able to inject faults into different types of cells including standard library cells as well as into two types of radiation tolerant flip-flops. Experimenting with radiation-tolerant flip-flops faults were injected illuminating areas with PMOS transistors in OFF state. We determined the cells areas, which were sensitive to the laser FI attacks. Only few works discussed this aspect in the past determining NMOS transistors as the sensitive part of the logic cells. Knowledge about the areas which are sensitive to the laser FI attacks can be generalized experimenting with other technologies and used in future by designers to implement corresponding countermeasure(s) at the initial stage of chip development.
(21) Horizontal Attack against EC kP Accelerator under Laser Illumination
D. Petryk, I. Kabin, P. Langendörfer, Z. Dyka
Electronics (MDPI) 14(10), 2072 (2025)
DOI: 10.3390/electronics14102072, (Total Resilience)
Devices employing cryptographic approaches have to be resistant to physical attacks. Side-Channel Analysis (SCA) and Fault Injection (FI) attacks are frequently used to reveal cryptographic keys. In this paper, we present a combined SCA and laser illumination attack against an Elliptic Curve Scalar Multiplication accelerator, while using different equipment for the measurement of its power traces, i.e., we performed the measurements using a current probe from Riscure and a differential probe from Teledyne LeCroy, with an attack success of 70% and 90%, respectively. Our experiments showed that laser illumination increased the power consumption of the chip, especially its static power consumption, but the success of the horizontal power analysis attacks changed insignificantly. After applying 100% of the laser beam output power and illuminating the smallest area of 143 µm2, we observed an offset of 17 mV in the measured trace. We assume that using a laser with a high laser beam power, as well as concentrating on measuring and analysing only static current, can significantly improve the attack’s success. The attacks exploiting the Static Current under Laser Illumination (SCuLI attacks) are novel, and their potential has not yet been fully investigated. These attacks can be especially dangerous against cryptographic chips manufactured in downscaling technologies. If such attacks are feasible, appropriate countermeasures have to be proposed in the future.
(22) Horizontal Attack against EC kP Accelerator under Laser Illumination
D. Petryk, I. Kabin, P. Langendörfer, Z. Dyka
Electronics (MDPI) 14(10), 2072 (2025)
DOI: 10.3390/electronics14102072, (Resilient Systems)
Devices employing cryptographic approaches have to be resistant to physical attacks. Side-Channel Analysis (SCA) and Fault Injection (FI) attacks are frequently used to reveal cryptographic keys. In this paper, we present a combined SCA and laser illumination attack against an Elliptic Curve Scalar Multiplication accelerator, while using different equipment for the measurement of its power traces, i.e., we performed the measurements using a current probe from Riscure and a differential probe from Teledyne LeCroy, with an attack success of 70% and 90%, respectively. Our experiments showed that laser illumination increased the power consumption of the chip, especially its static power consumption, but the success of the horizontal power analysis attacks changed insignificantly. After applying 100% of the laser beam output power and illuminating the smallest area of 143 µm2, we observed an offset of 17 mV in the measured trace. We assume that using a laser with a high laser beam power, as well as concentrating on measuring and analysing only static current, can significantly improve the attack’s success. The attacks exploiting the Static Current under Laser Illumination (SCuLI attacks) are novel, and their potential has not yet been fully investigated. These attacks can be especially dangerous against cryptographic chips manufactured in downscaling technologies. If such attacks are feasible, appropriate countermeasures have to be proposed in the future.
(23) 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)
(24) 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)
(25) 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)
(26) 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)
(27) 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)
(28) 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.
(29) 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.
(30) Atomic Patterns: Field Operation Distinguishability on Cryptographic ASICs
A.A. Sigourou, Z. Dyka, P. Langendörfer, I. Kabin
Proc. IEEE International Conference on Cyber Security and Resilience (CSR 2025), 990 (2025)
DOI: 10.1109/CSR64739.2025.11130154, (Total Resilience)
(31) Atomic Patterns: Field Operation Distinguishability on Cryptographic ASICs
A.A. Sigourou, Z. Dyka, P. Langendörfer, I. Kabin
Proc. IEEE International Conference on Cyber Security and Resilience (CSR 2025), 990 (2025)
DOI: 10.1109/CSR64739.2025.11130154, (Resilient Systems)
(32) Revisiting Atomic Patterns for Elliptic Curve Scalar Multiplication Revealing Inherent Vulnerability to Simple SCA
A.A. Sigourou, Z. Dyka, S.H. Li , P. Langendörfer, I. Kabin
Proc. 12th IFIP International Conference on New Technologies, Mobility and Security (NTMS 2025), 252 (2025)
DOI: 10.1109/NTMS65597.2025.11076762, (Total Resilience)
(33) 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)
(34) 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)
(35) 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)
(36) 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)
(37) 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)
(38) Challenges in Developing Modular AI Applications
K. Woloszyn, K. Piotrowski, K. Turchan
Proc. 21. GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2024), 18 (2025)
(39) 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)
(40) 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)
(41) 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)
(42) 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)
