Script list Publications
(1) Advantages of Unsupervised Learning Analysis Methods in Single-Trace SCA Attacks
M. Aftowicz, I. Kabin, Z. Dyka, P. Langendörfer
Microprocessors and Microsystems 105, 104994 (2024)
DOI: 10.1016/j.micpro.2023.104994, (Total Resilience)
Machine learning techniques are commonly employed in the context of Side Channel Analysis attacks. The clustering algorithms can be successfully used as classifiers in single execution attacks against implementations of Elliptic Curve point multiplication known as kP operation. They can distinguish between the processing of ‘ones’ and ‘zeros’ during secret scalar processing in the binary kP algorithm. The successful SCA performed by designers can aid in recognizing the leakage sources in cryptographic designs and lead to improvement of the cryptographic implementations. In this work we investigate the influence of the hamming weight of scalar k on the success rate of the single-trace attack. We used the clustering method K-means and the statistical method the comparison to the mean. We analysed simulated power traces and power traces of an FPGA implementation to conclude that K-means, unlike the comparison to the mean, was able to deal with extracting the scalar even when it is consisted of less than 30% of ‘ones’ and more than 70% of ‘ones’.
(2) Non-Profiled Unsupervised Horizontal Iterative Attack Against Hardware Elliptic Curve Scalar Multiplication using Machine Learning
M. Aftowicz, I. Kabin, Z. Dyka, P. Langendörfer
Future Internet (MDPI) 16(2), 45 (2024)
DOI: 10.3390/fi16020045
While IoT technology makes industries, cities, and homes smarter, it also opens the door to security risks. With the right equipment and physical access to the devices, the attacker can leverage side-channel information, like timing, power consumption or electromagnetic emanation to compromise cryptographic operations and extract the secret key. This work presents a side-channel analysis of a cryptographic hardware accelerator for Elliptic Curve Scalar Multiplication operation, implemented in a Field Programmable Gate Array and as an Application Specific Integrated Circuit. The presented framework consists in initial key extraction using a state-of-the-art statistical horizontal attack and is followed by regularized Artificial Neural Networks, which take as input the partially incorrect key guesses from the horizontal attack and correct them iteratively. The initial correctness of the horizontal attack, measured as the fraction of correctly extracted bits of the secret key, was improved from 75% to 98% by applying the iterative learning.
(3) Silent Sabotage: A Stealthy Control Logic Injection in IIoT Systems
W. Alsabbagh, C. Kim, P. Langendörfer
Proc. 5th Silicon Valley Cybersecurity Conference (SVCC 2024)
DOI: 10.1109/SVCC61185.2024.10637363
(4) Investigating the Security of OpenPLC: Vulnerabilities, Attacks, and Mitigation Solutions
W. Alsabbagh, C. Kim, P. Langendörfer
IEEE Access 12, 11561 (2024)
DOI: 10.1109/ACCESS.2024.3356051
Open-source Programmable Logic Controller (OpenPLC) has recently gained substantial interest within both the research and industrial communities. It presents an affordable and practical alternative solution for the high-cost of real hardware PLCs. But the project has not fulfilled the security level that is required for Industrial Control Systems (ICSs). Therefore, it becomes imperative to scrutinize the project's security landscape, identifying vulnerabilities that could expose OpenPLC and its related systems to threats. This article focuses precisely on this objective, undertaking intensive investigations into OpenPLC. To substantiate our findings, we conduct a sophisticated control logic injection attack, compromising the user authentication and modifying the program executed by the OpenPLC. Additionally, based on our results, we introduce a security-enhanced OpenPLC software called OpenPLC Aqua. Our developed software is equipped with a set of security solutions designed specifically to address the vulnerabilities to which current OpenPLC versions are prone.
(5) Unsupervised and Semisupervised Machine Learning Frameworks for Multiclass Tool Wear Recognition
M. Assafo, P. Langendörfer
IEEE Open Journal of the Industrial Electronics Society 5, 993 (2024)
DOI: 10.1109/OJIES.2024.3455264, (iCampus II)
Tool condition monitoring (TCM) is crucial to ensure good quality products and avoid downtime. Machine learning has proven to be vital for TCM. However, existing works are predominately based on supervised learning, which hinders their applicability in real-world manufacturing settings where data labeling is cumbersome and costly with in-service machines. Additionally, the existing unsupervised solutions mostly handle binary decision-based TCM which is unable to fully reflect the dynamics of tool wear progression. To address these issues, we propose different unsupervised and semisupervised 5-class tool wear recognition frameworks to handle fully-unlabeled and partially-labeled data, respectively. The underlying methods include Laplacian score, sparse autoencoder (SAE), stacked SAE (SSAE), self-organizing map, Softmax, support vector machine, and random forest. For the semisupervised frameworks, we considered designs where labeled data influence only feature learning, classifier building, or both. We also investigated different training configurations of SSAE regarding the supervision level induced in its deep learning. We applied the frameworks on two run-to-failure datasets of milling tools, recorded using a microphone and an accelerometer. Single sensor and multisensor data under different percentages of labeled training data were considered in the evaluation. The results showed which of the frameworks led to the best predictive performance under which data settings, and highlighted the significance of sensor fusion and discriminative feature representations in combating the unavailability and scarcity of labels, among other findings. The highest macro-F1 achieved for the two datasets with fully-unlabeled data reached 87.52% and 75.80%, respectively, and over 90% when only 25% of the training observations were labeled.
(6) Real-Time Jamming Detection, Classification and Logging using Computer Vision in 5G Private Networks
R. Chitauro, M. Brzozowski, O. Yener, P. Langendörfer
Proc. 19th International Symposium on Wireless Communication Systems (ISWCS 2024), (2024)
DOI: 10.1109/ISWCS61526.2024.10639080, (EMiL)
(7) Out of Distribution Generalization: KPI vs Spectrogram Based Jamming Classification in 5G
R. Chitauro, M. Brzozowski, O. Yener, P. Langendörfer
Proc. 32nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2024), (2024)
(Open 6G Hub)
(8) Out of Distribution Generalization: KPI vs Spectrogram Based Jamming Classification in 5G
R. Chitauro, M. Brzozowski, O. Yener, P. Langendörfer
Proc. 32nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2024), (2024)
(EMiL)
(9) Real-Time Jamming Detection, Classification and Logging using Computer Vision in 5G Private Networks
R. Chitauro, M. Brzozowski, O. Yener, P. Langendörfer
Proc. 19th International Symposium on Wireless Communication Systems (ISWCS 2024), (2024)
DOI: 10.1109/ISWCS61526.2024.10639080, (Open 6G Hub)
(10) On the Influence of Cell Libraries and Other Parameters to SCA Resistance of Crypto IP Cores
Z. Dyka, I. Kabin, M. Brzozowski, G. Panic, C. Calligaro, M. Krstic, P. Langendörfer
Proc. 13th Mediterranean Conference on Embedded Computing (MECO 2024), 80 (2024)
DOI: 10.1109/MECO62516.2024.10577776, (Total Resilience)
(11) Security Mean Distribution in WSNs for Cooperative Schemes
B. Förster, T. Hinze, P. Langendörfer
Proc. iCampus Cottbus Conference (iCCC 2024), 38 (2024)
DOI: 10.5162/iCCC2024/1.2
(12) Determining Distributions of Security Means for WSNs based on the Model of a Neighbourhood Watch
B. Förster, P. Langendörfer, T. Hinze
IEEE Access 12, 74343 (2024)
DOI: 10.1109/ACCESS.2024.3404816
Neighbourhood watch is a concept allowing a community to distribute a complex security task in between all members. Members carry out security tasks in a distributed and cooperative manner ensuring their mutual security and reducing the individual workload while increasing the overall security of the community. Wireless sensor networks (WSNs) are composed of resource-constraint independent battery driven computers as nodes communicating wirelessly. Security in WSNs is essential to prevent attackers from eavesdropping, tampering monitoring results or denying critical nodes from providing their services and potentially cutting off larger network parts. The resource-constraint nature of sensor nodes prevents them from running full-fledged security protocols. Instead, it is necessary to assess the most significant security threats and implement specialised security solutions. A neighbourhood watch inspired distributed security scheme for WSNs has been introduced by Langendörfer aiming to increase the variety of attacks a WSN can fend off. The framework intends to statically distribute requirement-based selections of online security means intended to cooperate in close proximity on large-scale static homogeneous WSNs. A framework of such complexity has to be designed in multiple steps. We determine suitable distributions of security means based on graph partitioning concepts. The partitioning algorithms we provide are NP-hard. To evaluate their computability, we implement them as 0-1 linear programs (LPs) and test them on WSN models generated with our novel λ-precision unit disk graph (UDG) generator.
(13) Minimizing the Latency of Freezing of Gait Detection on Wearable Devices
A. Haddadi Esfahani, O. Maye, M. Frohberg, St. Ortmann, P. Langendörfer
Proc. iCampus Cottbus Conference (iCCC 2024), 49 (2024)
DOI: 10.5162/iCCC2024/2.2, (FastGait)
(14) Real Time Detection of Freezing of Gait of Parkinson Patients based on Machine Learning Running on a Body Worn Device
A. Haddadi Esfahani, O. Maye, M. Frohberg, M. Speh, M. Jöbges, P. Langendörfer
Procedia Computer Science 239, 177 (2024)
DOI: 10.1016/j.procs.2024.06.160, (FastGait)
In this work, we present a system that detects Freezing of Gait Detection (FOG) that uses of a single wearable inertial sensor to automatically evaluate a Parkinson’s patient’s gait instability and detect FOG in real-time. A wearable vibrator is our cueing system which is triggered by the FOG detection whenever a FOG episode takes place. The vibration impulses help the patient to prevent FOG by switching to voluntarily movement execution. Sensor data were collected from nine patients with Parkinson’s disease performing Unified Parkinson’s Disease Rating Scale (UPDRS) test under the supervision of a clinical expert. Along with data recording, a video was taken from patient’s parkour. The data were labeled through the recorded video of the patient’s tests and FOG and non-FOG data were assigned. A machine learning model using a deep Long-Short-Term-Memory (LSTM) employ the accelerometer data from the sensor and the inference leads to a FOG or non-FOG classification. The FOG detection model is pruned, quantized and used for real-time inference on Google Coral board worn on the patient’ body. The model deployed on a Google coral board sends a trigger to the cuing device right after the FOG detection and the patient get alert for the happening FOG. The individualized model for one-second windows applied in this work performed an average of 91.5% of sensitivity and 86.5% specificity for models running on PC and 91.7% of sensitivity and 86.7% of specificity for the models tested on Google coral with the latency of 50 millisecond on real-time testing.
(15) Exploiting Static Power Consumption in Side-Channel Analysis
I. Kabin, P. Langendörfer, Z. Dyka
Proc. 25th IEEE Latin American Test Symposium (LATS 2024), (2024)
DOI: 10.1109/LATS62223.2024.10534604, (Total Resilience)
(16) Stealth Attacks on PCBs: An Experimental Plausibility Analysis
I. Kabin, J. Schäffner, A.A. Sigourou, D. Petryk, Z. Dyka,, D. Klein, S. Freud, P. Langendörfer
Proc. IEEE International Conference on Cyber Security and Resilience: Workshop on Hardware Cybersecurity Systems (HACS 2024), 905 (2024)
DOI: 10.1109/CSR61664.2024.10679465, (PANDA)
(17) Static Power Consumption as a New Side-Channel Analysis Threat to Elliptic Curve Cryptography Implementations
I. Kabin, Z. Dyka, A.A. Sigourou, P. Langendörfer
Proc. IEEE International Conference on Cyber Security and Resilience: Workshop on Hardware Cybersecurity Systems (HACS 2024), 884 (2024)
DOI: 10.1109/CSR61664.2024.10679507, (Total Resilience)
(18) IoT and Data Spaces as a Highly Distributed Measurement and Control System
I. Koropiecki, K. Piotrowski
Proc. 15th Konferencja Naukowa Systemy Pomiarowe w badaniach naukowych i w przemyśle (SP 2024), 41 (2024)
(19) Energy Flexibility in the Context of Measurement and Control
M. Krysik, K. Piotrowski
Proc. 15th Konferencja Naukowa Systemy Pomiarowe w badaniach naukowych i w przemyśle (SP 2024), 53 (2024)
(20) WindowGuardian: Return Address Integrity for ESP32 Microcontrollers with Xtensa Processors using AES and Register Windows
K. Lehniger, S.P. Raghunathan, P. Langendörfer
Proc. 12th International Conference on Cyber-Physical Systems and Internet-of-Things (CPS & IoT 2024), 29 (2024)
DOI: 10.1109/MECO62516.2024.10577840
(21) Kangaroo Protocol as a Sensor: Monitoring Parameters of Wireless Sensor Networks
J. Maj, K. Piotrowski
Proc. 15th Konferencja Naukowa Systemy Pomiarowe w badaniach naukowych i w przemyśle (SP 2024), 85 (2024)
(22) Towards Secure and Reliable Heterogeneous Real-Time Telemetry Communication in Autonomous UAV Swarms
P. Mykytyn, M. Brzozowski, Z. Dyka, P. Langendörfer
Proc. iCampus Cottbus Conference (iCCC 2024), 165 (2024)
DOI: 10.5162/iCCC2024/P15, (iCampus)
(23) A Survey on Sensor- and Communication-based Issues of Autonomous UAVs
P. Mykytyn, M. Brzozowski, Z. Dyka, P. Langendörfer
Computer Modeling in Engineering & Sciences 138(2), 1019 (2024)
DOI: 10.32604/cmes.2023.029075, (iCampus II)
The application field for Unmanned Aerial Vehicle (UAV) technology and its adoption rate have been increasing steadily in the past years. Decreasing cost of commercial drones has enabled their use at a scale broader than ever before. However, increasing the complexity of UAVs and decreasing the cost, both contribute to a lack of implemented security measures and raise new security and safety concerns. For instance, the issue of implausible or tampered UAV sensor measurements is barely addressed in the current research literature and thus, requires more attention from the research community. The goal of this survey is to extensively review state-of-the-art literature regarding common sensor- and communication-based vulnerabilities, existing threats, and active or passive cyber-attacks against UAVs, as well as shed light on the research gaps in the literature. In this work, we describe the Unmanned Aerial System (UAS) architecture to point out the origination sources for security and safety issues. We evaluate the coverage and completeness of each related research work in a comprehensive comparison table as well as classify the threats, vulnerabilities and cyber-attacks into sensor-based and communication-based categories. Additionally, for each individual cyber-attack, we describe existing countermeasures or detection mechanisms and provide a list of requirements to ensure UAV’s security and safety. We also address the problem of implausible sensor measurements and introduce the idea of a plausibility check for sensor data. By doing so, we discover additional measures to improve security and safety and report on a research niche that is not well represented in the current research literature.
(24) Precise Sensors for Localization in the Drone Swarm
M. Nattke, R. Rotta, R. Natarov, O. Archila, P. Mykytyn
Proc. iCampus Cottbus Conference (iCCC 2024), 166 (2024)
DOI: 10.5162/iCCC2024/P16, (iCampus II)
(25) Employing Optical Beam-Induced Current Measurement in Side-Channel Analysis
D. Petryk, I. Kabin, J. Bělohoubek, P. Fišer, J. Schmidt, M. Krstic, Z. Dyka
Proc. 36. ITG/GMM/GI-Workshop Testmethoden und Zuverlässigkeit von Schaltungen und Systemen (TuZ 2024), 15 (2024)
(Total Resilience)
(26) On the Importance of Reproducibility of Experimental Results Especially in the Domain of Security
D. Petryk, I. Kabin, P. Langendörfer, Z. Dyka
Proc. 13th Mediterranean Conference on Embedded Computing (MECO 2024), 311 (2024)
DOI: 10.1109/MECO62516.2024.10577919, (Total Resilience)
(27) Distributed Data-Centric Application Logic Layer
K. Turchan, K. Piotrowski
Proc. 15th Konferencja Naukowa Systemy Pomiarowe w badaniach naukowych i w przemyśle (SP 2024), 145 (2024)
(28) Automated Modular Application Development
K. Woloszyn, K. Piotrowski
Proc. 15th Konferencja Naukowa Systemy Pomiarowe w badaniach naukowych i w przemyśle (SP 2024), 165 (2024)
(29) Modular Development of Data-Centric Applications for Edge Devices
P. Zielony, K. Piotrowski
Proc. 15th Konferencja Naukowa Systemy Pomiarowe w badaniach naukowych i w przemyśle (SP 2024), 173 (2024)
(30) Distributed Data-Centric Maintenence and Management Layer for WSN
P. Zielony, K. Piotrowski
Proc. 15th Konferencja Naukowa Systemy Pomiarowe w badaniach naukowych i w przemyśle (SP 2024), 169 (2024)
M. Aftowicz, I. Kabin, Z. Dyka, P. Langendörfer
Microprocessors and Microsystems 105, 104994 (2024)
DOI: 10.1016/j.micpro.2023.104994, (Total Resilience)
Machine learning techniques are commonly employed in the context of Side Channel Analysis attacks. The clustering algorithms can be successfully used as classifiers in single execution attacks against implementations of Elliptic Curve point multiplication known as kP operation. They can distinguish between the processing of ‘ones’ and ‘zeros’ during secret scalar processing in the binary kP algorithm. The successful SCA performed by designers can aid in recognizing the leakage sources in cryptographic designs and lead to improvement of the cryptographic implementations. In this work we investigate the influence of the hamming weight of scalar k on the success rate of the single-trace attack. We used the clustering method K-means and the statistical method the comparison to the mean. We analysed simulated power traces and power traces of an FPGA implementation to conclude that K-means, unlike the comparison to the mean, was able to deal with extracting the scalar even when it is consisted of less than 30% of ‘ones’ and more than 70% of ‘ones’.
(2) Non-Profiled Unsupervised Horizontal Iterative Attack Against Hardware Elliptic Curve Scalar Multiplication using Machine Learning
M. Aftowicz, I. Kabin, Z. Dyka, P. Langendörfer
Future Internet (MDPI) 16(2), 45 (2024)
DOI: 10.3390/fi16020045
While IoT technology makes industries, cities, and homes smarter, it also opens the door to security risks. With the right equipment and physical access to the devices, the attacker can leverage side-channel information, like timing, power consumption or electromagnetic emanation to compromise cryptographic operations and extract the secret key. This work presents a side-channel analysis of a cryptographic hardware accelerator for Elliptic Curve Scalar Multiplication operation, implemented in a Field Programmable Gate Array and as an Application Specific Integrated Circuit. The presented framework consists in initial key extraction using a state-of-the-art statistical horizontal attack and is followed by regularized Artificial Neural Networks, which take as input the partially incorrect key guesses from the horizontal attack and correct them iteratively. The initial correctness of the horizontal attack, measured as the fraction of correctly extracted bits of the secret key, was improved from 75% to 98% by applying the iterative learning.
(3) Silent Sabotage: A Stealthy Control Logic Injection in IIoT Systems
W. Alsabbagh, C. Kim, P. Langendörfer
Proc. 5th Silicon Valley Cybersecurity Conference (SVCC 2024)
DOI: 10.1109/SVCC61185.2024.10637363
(4) Investigating the Security of OpenPLC: Vulnerabilities, Attacks, and Mitigation Solutions
W. Alsabbagh, C. Kim, P. Langendörfer
IEEE Access 12, 11561 (2024)
DOI: 10.1109/ACCESS.2024.3356051
Open-source Programmable Logic Controller (OpenPLC) has recently gained substantial interest within both the research and industrial communities. It presents an affordable and practical alternative solution for the high-cost of real hardware PLCs. But the project has not fulfilled the security level that is required for Industrial Control Systems (ICSs). Therefore, it becomes imperative to scrutinize the project's security landscape, identifying vulnerabilities that could expose OpenPLC and its related systems to threats. This article focuses precisely on this objective, undertaking intensive investigations into OpenPLC. To substantiate our findings, we conduct a sophisticated control logic injection attack, compromising the user authentication and modifying the program executed by the OpenPLC. Additionally, based on our results, we introduce a security-enhanced OpenPLC software called OpenPLC Aqua. Our developed software is equipped with a set of security solutions designed specifically to address the vulnerabilities to which current OpenPLC versions are prone.
(5) Unsupervised and Semisupervised Machine Learning Frameworks for Multiclass Tool Wear Recognition
M. Assafo, P. Langendörfer
IEEE Open Journal of the Industrial Electronics Society 5, 993 (2024)
DOI: 10.1109/OJIES.2024.3455264, (iCampus II)
Tool condition monitoring (TCM) is crucial to ensure good quality products and avoid downtime. Machine learning has proven to be vital for TCM. However, existing works are predominately based on supervised learning, which hinders their applicability in real-world manufacturing settings where data labeling is cumbersome and costly with in-service machines. Additionally, the existing unsupervised solutions mostly handle binary decision-based TCM which is unable to fully reflect the dynamics of tool wear progression. To address these issues, we propose different unsupervised and semisupervised 5-class tool wear recognition frameworks to handle fully-unlabeled and partially-labeled data, respectively. The underlying methods include Laplacian score, sparse autoencoder (SAE), stacked SAE (SSAE), self-organizing map, Softmax, support vector machine, and random forest. For the semisupervised frameworks, we considered designs where labeled data influence only feature learning, classifier building, or both. We also investigated different training configurations of SSAE regarding the supervision level induced in its deep learning. We applied the frameworks on two run-to-failure datasets of milling tools, recorded using a microphone and an accelerometer. Single sensor and multisensor data under different percentages of labeled training data were considered in the evaluation. The results showed which of the frameworks led to the best predictive performance under which data settings, and highlighted the significance of sensor fusion and discriminative feature representations in combating the unavailability and scarcity of labels, among other findings. The highest macro-F1 achieved for the two datasets with fully-unlabeled data reached 87.52% and 75.80%, respectively, and over 90% when only 25% of the training observations were labeled.
(6) Real-Time Jamming Detection, Classification and Logging using Computer Vision in 5G Private Networks
R. Chitauro, M. Brzozowski, O. Yener, P. Langendörfer
Proc. 19th International Symposium on Wireless Communication Systems (ISWCS 2024), (2024)
DOI: 10.1109/ISWCS61526.2024.10639080, (EMiL)
(7) Out of Distribution Generalization: KPI vs Spectrogram Based Jamming Classification in 5G
R. Chitauro, M. Brzozowski, O. Yener, P. Langendörfer
Proc. 32nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2024), (2024)
(Open 6G Hub)
(8) Out of Distribution Generalization: KPI vs Spectrogram Based Jamming Classification in 5G
R. Chitauro, M. Brzozowski, O. Yener, P. Langendörfer
Proc. 32nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2024), (2024)
(EMiL)
(9) Real-Time Jamming Detection, Classification and Logging using Computer Vision in 5G Private Networks
R. Chitauro, M. Brzozowski, O. Yener, P. Langendörfer
Proc. 19th International Symposium on Wireless Communication Systems (ISWCS 2024), (2024)
DOI: 10.1109/ISWCS61526.2024.10639080, (Open 6G Hub)
(10) On the Influence of Cell Libraries and Other Parameters to SCA Resistance of Crypto IP Cores
Z. Dyka, I. Kabin, M. Brzozowski, G. Panic, C. Calligaro, M. Krstic, P. Langendörfer
Proc. 13th Mediterranean Conference on Embedded Computing (MECO 2024), 80 (2024)
DOI: 10.1109/MECO62516.2024.10577776, (Total Resilience)
(11) Security Mean Distribution in WSNs for Cooperative Schemes
B. Förster, T. Hinze, P. Langendörfer
Proc. iCampus Cottbus Conference (iCCC 2024), 38 (2024)
DOI: 10.5162/iCCC2024/1.2
(12) Determining Distributions of Security Means for WSNs based on the Model of a Neighbourhood Watch
B. Förster, P. Langendörfer, T. Hinze
IEEE Access 12, 74343 (2024)
DOI: 10.1109/ACCESS.2024.3404816
Neighbourhood watch is a concept allowing a community to distribute a complex security task in between all members. Members carry out security tasks in a distributed and cooperative manner ensuring their mutual security and reducing the individual workload while increasing the overall security of the community. Wireless sensor networks (WSNs) are composed of resource-constraint independent battery driven computers as nodes communicating wirelessly. Security in WSNs is essential to prevent attackers from eavesdropping, tampering monitoring results or denying critical nodes from providing their services and potentially cutting off larger network parts. The resource-constraint nature of sensor nodes prevents them from running full-fledged security protocols. Instead, it is necessary to assess the most significant security threats and implement specialised security solutions. A neighbourhood watch inspired distributed security scheme for WSNs has been introduced by Langendörfer aiming to increase the variety of attacks a WSN can fend off. The framework intends to statically distribute requirement-based selections of online security means intended to cooperate in close proximity on large-scale static homogeneous WSNs. A framework of such complexity has to be designed in multiple steps. We determine suitable distributions of security means based on graph partitioning concepts. The partitioning algorithms we provide are NP-hard. To evaluate their computability, we implement them as 0-1 linear programs (LPs) and test them on WSN models generated with our novel λ-precision unit disk graph (UDG) generator.
(13) Minimizing the Latency of Freezing of Gait Detection on Wearable Devices
A. Haddadi Esfahani, O. Maye, M. Frohberg, St. Ortmann, P. Langendörfer
Proc. iCampus Cottbus Conference (iCCC 2024), 49 (2024)
DOI: 10.5162/iCCC2024/2.2, (FastGait)
(14) Real Time Detection of Freezing of Gait of Parkinson Patients based on Machine Learning Running on a Body Worn Device
A. Haddadi Esfahani, O. Maye, M. Frohberg, M. Speh, M. Jöbges, P. Langendörfer
Procedia Computer Science 239, 177 (2024)
DOI: 10.1016/j.procs.2024.06.160, (FastGait)
In this work, we present a system that detects Freezing of Gait Detection (FOG) that uses of a single wearable inertial sensor to automatically evaluate a Parkinson’s patient’s gait instability and detect FOG in real-time. A wearable vibrator is our cueing system which is triggered by the FOG detection whenever a FOG episode takes place. The vibration impulses help the patient to prevent FOG by switching to voluntarily movement execution. Sensor data were collected from nine patients with Parkinson’s disease performing Unified Parkinson’s Disease Rating Scale (UPDRS) test under the supervision of a clinical expert. Along with data recording, a video was taken from patient’s parkour. The data were labeled through the recorded video of the patient’s tests and FOG and non-FOG data were assigned. A machine learning model using a deep Long-Short-Term-Memory (LSTM) employ the accelerometer data from the sensor and the inference leads to a FOG or non-FOG classification. The FOG detection model is pruned, quantized and used for real-time inference on Google Coral board worn on the patient’ body. The model deployed on a Google coral board sends a trigger to the cuing device right after the FOG detection and the patient get alert for the happening FOG. The individualized model for one-second windows applied in this work performed an average of 91.5% of sensitivity and 86.5% specificity for models running on PC and 91.7% of sensitivity and 86.7% of specificity for the models tested on Google coral with the latency of 50 millisecond on real-time testing.
(15) Exploiting Static Power Consumption in Side-Channel Analysis
I. Kabin, P. Langendörfer, Z. Dyka
Proc. 25th IEEE Latin American Test Symposium (LATS 2024), (2024)
DOI: 10.1109/LATS62223.2024.10534604, (Total Resilience)
(16) Stealth Attacks on PCBs: An Experimental Plausibility Analysis
I. Kabin, J. Schäffner, A.A. Sigourou, D. Petryk, Z. Dyka,, D. Klein, S. Freud, P. Langendörfer
Proc. IEEE International Conference on Cyber Security and Resilience: Workshop on Hardware Cybersecurity Systems (HACS 2024), 905 (2024)
DOI: 10.1109/CSR61664.2024.10679465, (PANDA)
(17) Static Power Consumption as a New Side-Channel Analysis Threat to Elliptic Curve Cryptography Implementations
I. Kabin, Z. Dyka, A.A. Sigourou, P. Langendörfer
Proc. IEEE International Conference on Cyber Security and Resilience: Workshop on Hardware Cybersecurity Systems (HACS 2024), 884 (2024)
DOI: 10.1109/CSR61664.2024.10679507, (Total Resilience)
(18) IoT and Data Spaces as a Highly Distributed Measurement and Control System
I. Koropiecki, K. Piotrowski
Proc. 15th Konferencja Naukowa Systemy Pomiarowe w badaniach naukowych i w przemyśle (SP 2024), 41 (2024)
(19) Energy Flexibility in the Context of Measurement and Control
M. Krysik, K. Piotrowski
Proc. 15th Konferencja Naukowa Systemy Pomiarowe w badaniach naukowych i w przemyśle (SP 2024), 53 (2024)
(20) WindowGuardian: Return Address Integrity for ESP32 Microcontrollers with Xtensa Processors using AES and Register Windows
K. Lehniger, S.P. Raghunathan, P. Langendörfer
Proc. 12th International Conference on Cyber-Physical Systems and Internet-of-Things (CPS & IoT 2024), 29 (2024)
DOI: 10.1109/MECO62516.2024.10577840
(21) Kangaroo Protocol as a Sensor: Monitoring Parameters of Wireless Sensor Networks
J. Maj, K. Piotrowski
Proc. 15th Konferencja Naukowa Systemy Pomiarowe w badaniach naukowych i w przemyśle (SP 2024), 85 (2024)
(22) Towards Secure and Reliable Heterogeneous Real-Time Telemetry Communication in Autonomous UAV Swarms
P. Mykytyn, M. Brzozowski, Z. Dyka, P. Langendörfer
Proc. iCampus Cottbus Conference (iCCC 2024), 165 (2024)
DOI: 10.5162/iCCC2024/P15, (iCampus)
(23) A Survey on Sensor- and Communication-based Issues of Autonomous UAVs
P. Mykytyn, M. Brzozowski, Z. Dyka, P. Langendörfer
Computer Modeling in Engineering & Sciences 138(2), 1019 (2024)
DOI: 10.32604/cmes.2023.029075, (iCampus II)
The application field for Unmanned Aerial Vehicle (UAV) technology and its adoption rate have been increasing steadily in the past years. Decreasing cost of commercial drones has enabled their use at a scale broader than ever before. However, increasing the complexity of UAVs and decreasing the cost, both contribute to a lack of implemented security measures and raise new security and safety concerns. For instance, the issue of implausible or tampered UAV sensor measurements is barely addressed in the current research literature and thus, requires more attention from the research community. The goal of this survey is to extensively review state-of-the-art literature regarding common sensor- and communication-based vulnerabilities, existing threats, and active or passive cyber-attacks against UAVs, as well as shed light on the research gaps in the literature. In this work, we describe the Unmanned Aerial System (UAS) architecture to point out the origination sources for security and safety issues. We evaluate the coverage and completeness of each related research work in a comprehensive comparison table as well as classify the threats, vulnerabilities and cyber-attacks into sensor-based and communication-based categories. Additionally, for each individual cyber-attack, we describe existing countermeasures or detection mechanisms and provide a list of requirements to ensure UAV’s security and safety. We also address the problem of implausible sensor measurements and introduce the idea of a plausibility check for sensor data. By doing so, we discover additional measures to improve security and safety and report on a research niche that is not well represented in the current research literature.
(24) Precise Sensors for Localization in the Drone Swarm
M. Nattke, R. Rotta, R. Natarov, O. Archila, P. Mykytyn
Proc. iCampus Cottbus Conference (iCCC 2024), 166 (2024)
DOI: 10.5162/iCCC2024/P16, (iCampus II)
(25) Employing Optical Beam-Induced Current Measurement in Side-Channel Analysis
D. Petryk, I. Kabin, J. Bělohoubek, P. Fišer, J. Schmidt, M. Krstic, Z. Dyka
Proc. 36. ITG/GMM/GI-Workshop Testmethoden und Zuverlässigkeit von Schaltungen und Systemen (TuZ 2024), 15 (2024)
(Total Resilience)
(26) On the Importance of Reproducibility of Experimental Results Especially in the Domain of Security
D. Petryk, I. Kabin, P. Langendörfer, Z. Dyka
Proc. 13th Mediterranean Conference on Embedded Computing (MECO 2024), 311 (2024)
DOI: 10.1109/MECO62516.2024.10577919, (Total Resilience)
(27) Distributed Data-Centric Application Logic Layer
K. Turchan, K. Piotrowski
Proc. 15th Konferencja Naukowa Systemy Pomiarowe w badaniach naukowych i w przemyśle (SP 2024), 145 (2024)
(28) Automated Modular Application Development
K. Woloszyn, K. Piotrowski
Proc. 15th Konferencja Naukowa Systemy Pomiarowe w badaniach naukowych i w przemyśle (SP 2024), 165 (2024)
(29) Modular Development of Data-Centric Applications for Edge Devices
P. Zielony, K. Piotrowski
Proc. 15th Konferencja Naukowa Systemy Pomiarowe w badaniach naukowych i w przemyśle (SP 2024), 173 (2024)
(30) Distributed Data-Centric Maintenence and Management Layer for WSN
P. Zielony, K. Piotrowski
Proc. 15th Konferencja Naukowa Systemy Pomiarowe w badaniach naukowych i w przemyśle (SP 2024), 169 (2024)
