Publications 2025
Script list Publications
(1) 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)
(2) 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)
(3) 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.
(4) 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.
(5) 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)
(6) 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)
D. Petryk, I. Kabin, Z. Dyka
Proc. 37. ITG/GMM/GI-Workshop Testmethoden und Zuverlässigkeit von Schaltungen und Systemen (TuZ 2025), (2025)
(Total Resilience)
(2) 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)
(3) 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.
(4) 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.
(5) 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)
(6) 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)
