Publikationen 2024

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) A Holistic Approach for Characterization of SET Effects in a Standard Digital Cell Library
M. Andjelkovic, M. Krstic
Proc. 15th IEEE Latin America Symposium on Circuits and System (LASCAS 2024), (2024)
DOI: 10.1109/LASCAS60203.2024.10506165, (Open 6G Hub)

(3) A Holistic Approach for Characterization of SET Effects in a Standard Digital Cell Library
M. Andjelkovic, M. Krstic
Proc. 15th IEEE Latin America Symposium on Circuits and System (LASCAS 2024), (2024)
DOI: 10.1109/LASCAS60203.2024.10506165, (6G-TakeOff)

(4) Investigating the SEU Immunity of a Selectively-Hardened Complex Digital Filter
A. Breitenreiter, T. Lange, E. Pun, C. Schulze, F. Vargas, M. Krstic
Proc. 36. ITG/GMM/GI-Workshop Testmethoden und Zuverlässigkeit von Schaltungen und Systemen (TuZ 2024), 47 (2024)

(5) D-Band Active Antenna Array with Lens Enabling Quasi-Optical and Analogue Beam Reconfiguration for 6G Applications
M.A. Campo, S. Bruni, W. Wischmann, A. Lauer, A. Friedrich, M. Wleklinski, C. Oikonomopoulos-Zachos, O. Litschke, K. KrishneGowda, C. Herold, N. Moroni, W. Keusgen
18th European Conference on Antennas and Propagation (EuCAP 2024), (2024)
DOI: 10.23919/EuCAP60739.2024.10501233

(6) Failure Probability due to Radiation‑Induced Effects in FinFET SRAM Cells under Process Variations
V. Champac, H. Villacorta, R. Gomez‑Fuentes, F. Vargas, J. Segura
Journal of Electronic Testing 40, 75 (2024)
DOI: 10.1007/s10836-024-06102-0
This work studies radiation-induced effects in FinFET technology, the leading technology in advanced nodes for high-end embedded systems. As the fin height (HFIN) and the number of fins (NFIN) are two critical parameters in the development  of newer technologies, the soft-error robustness to radiation-induced effects in FinFET SRAM cells with HFIN and NFIN) is evaluated using Technology Computer-Aided Design (TCAD) tools. The ion strike direction and the process variations are considered. An analytical method to evaluate the failure probability of the memory cell due to radiation-induced effects under process variations is proposed. The amount of critical and collected charges of the memory cell are obtained with TCAD tools. The proposed method can be used to get insight into the robustness behavior of the memory cell with HFIN and NFIN and to guide the obtention of HFIN and NFIN parameters in developing new FinFET technologies.

(7) Neural Networks for Predicting the Optimal Beamforming Angles for Maximized Overall Wireless Network Capacity
P. Geranmayeh, E. Grass
Proc. 6th  International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2024), 77 (2024)
DOI: 10.1109/ICAIIC60209.2024.10463195, (5G-REMOTE)

(8) Machine Learning Based Beam Selection for Maximizing Wireless Network Capacity
P. Geranmayeh, E. Grass
IEEE Access 12, 45176 (2024)
DOI: 10.1109/ACCESS.2024.3381542, (5G-REMOTE)
In today’s and future wireless communications, especially in 5G and 6G networks, machine learning (ML) methods are crucial. Potentially, these techniques bring many benefits such as increased data throughput, improved security, reduced latency, and, on the whole, enhanced network efficiency. Furthermore, to facilitate the processing of large amounts of data in real-time situations, machine learning is used for various functions in wireless networks. This article aims to explore the significance and application of machine learning, with a particular focus on classic reinforcement learning, in the context of predicting optimal beam configurations within wireless communications scenarios. Our goal is to minimize interference between transmitters by finding the optimal beamforming angles. For this, ray tracing techniques are deployed. We see this research as a step forward towards integrating digital twin (DT) technology in network management and control. In this article, different machine learning methods are used and their performance is compared. Firstly, the most effective angles for beamforming, maximizing channel capacity are identified. Then, by using these methods and after verifying their accuracy, the optimal antenna angles in scenarios with an increased number of transmitters and receivers is found and evaluated.

(9) A Feature Extraction Approach for the Detection of Phishing Websites Using Machine Learning
S.C. Gundla, M.K. Praveen, M.J.K. Reddy, G. Gourav, A. Pankaj, Z. Stamenkovic, S.P. Raja
Journal of Circuits, Systems, and Computers (JCSC) 33(2), 2450031 (2024)
DOI: 10.1142/S0218126624500312, (BB-KI-Chips)
In this growing world of the internet, most of our daily routine tasks are somehow connected to the internet, from smartphones to IoT devices to cloud networks. Internet users are growing rapidly, and the internet is accessible to everyone from anywhere. Data phishing is a cyber security attack that uses deception to trick internet users to get their content and information. In these some malicious users try to steal personal data such as bank details, login credentials, credit card details, and health care information of others on the internet. They exploit users’ sensitive information using vulnerabilities. Information stealers are known as Phishers. Phishers use different techniques for phishing. One of the most common methods is to direct users to some false website to enter their login credentials and their details on these phishing sites. They look the same as the original websites. Phishers use these details to get access to the user’s accounts and hijack the user account for monetary purposes. Many internet users get into this trap of phishing sites and share their personal and sensitive details. In this paper, we will analyze and implement machine-learning techniques to detect phishing attacks. There are different methods to identify phishing attacks, one of them is by checking the URL address using Machine learning. ML is used as a way to teach a machine to differentiate between phishing and original site URLs. There are many different techniques to overcome this attack. This research paper aims to provide accurate and true phishing detection with less time complexity.

(10) Machine Learning Methodologies to Predict the Rresults of Simulation-Based Fault Injection
L. Lu, J.-C. Chen, M. Ulbricht, M. Krstic
IEEE Transactions on Circuits and Systems I 71(5), 1978 (2024)
DOI: 10.1109/TCSI.2024.3349928, (VE-HEP)
Simulation-based fault injection is a widely used technique for early-stage circuit reliability analysis. However, it consumes significant time, particularly for complex circuits. This paper introduces two Machine Learning (ML) methodologies to predict simulation-based fault injection outcomes at the gate level. The initial approach employs Neural Networks (NNs), extracting structural features from synthesis reports and simulation-related characteristics from Value Change Dump (VCD) waveforms. Nevertheless, NNs are restricted to learning from individual gate attributes. To exploit the comprehensive structure of entire circuits, we propose a method to convert circuits into graphs. This facilitates the utilization of Graph Neural Networks (GNNs) as advanced models, resulting in improved prediction performance. We select six open-source circuits with diverse complexities and functions to validate these methodologies and explore their adaptability across various circuits. Our experiments demonstrate the superior performance of GNNs compared to NNs in terms of prediction accuracy, efficiency in hyperparameter search, and the ability to address imbalanced datasets. Additionally, we investigate the feasibility of deploying the trained models to predict results in new circuits. Based on the experimental outcomes, we present an approach for leveraging the proposed methodology to accelerate simulation-based fault injection.

(11) Towards Critical Flip-Flop Identification for Soft-Error Tolerance with Graph Neural Networks
L. Lu, J.-C. Chen, M. Ulbricht, M. Krstic
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 43(4), 1135 (2024)
DOI: 10.1109/TCAD.2023.3331968, (VE-HEP)
Nanometer circuits are becoming increasingly susceptible to soft errors. Selective hardening is a less expensive technique to improve the reliability of circuits because it hardens the critical components instead of hardening an entire circuit. One challenge of selective hardening is efficiently and effectively identifying the critical parts in circuits. Simulation-based fault injection is commonly used but extremely time-consuming, especially for complex circuits. This article proposes an approach based on graph neural networks (GNNs) to identify critical flip-flops in circuits. GNNs can take advantage of the circuit’s structural features and the features of individual flip-flops. To convert the features into abstract data that can be fed into GNNs, we provide a feature extraction method that uses a graph model to represent the relevant features of the circuit. The method converts the target circuit into a graph representing its architecture. The graph also contains features of individual flip-flops extracted from the circuit’s netlist and the value change dump (VCD) waveforms of the test used for fault simulation. Additionally, we extract edge features in the graph to utilize the information on combinational gates on the path between flip-flops. Datasets generated based on two open-source RISC-V cores are used to validate the proposed approach. We compare the performance of different GNNs on them and discuss the contribution of edge features to their performance. Our experiments show that the prediction accuracy increases significantly with edge features. GraphSAGE and SAGE-GCN with edge features perform best among the selected GNNs. The highest accuracy we achieved on Ibex and RI5CY is 97.75% and 98.67%, respectively. We also provide a method to accelerate the process of critical flip-flop identification.

(12) 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)

(13) 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.

(14) An Efficient and Low Complexity Greedy Power Allocation Algorithm for URLLC Links
N. Odhah, E. Grass, R. Kraemer
Proc. iCampus Cottbus Conference (iCCC 2024), 175 (2024)
(5G-REMOTE)

(15) Unsourced Random Access Using ODMA and Polar Codes 
M. Özates, M. Kazemi, T.M. Duman
IEEE Wireless Communications Letters 13(4), 1044 (2024)
DOI: 10.1109/LWC.2024.3359270
We utilize the on-off division multiple access technique in unsourced random access and develop a high-performing solution operating on both Gaussian and fading multiple access channels based on polar coding. We propose a transmission structure where a fixed part of the transmission frame is utilized for pilot transmission while the users exploit a randomly-selected small fraction of the rest of the frame for data transmission. Our proposed scheme employs a simple, yet efficient pattern detection and single-user decoding algorithm at the receiver, which leads to a superior performance with similar complexity, or a competitive performance with a significantly lower complexity compared to the existing approaches.

(16) 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)

(17) Design and Analysis of an Adaptive Radiation Resilient RRAM Subsystem for Processing Systems in Satellites
D. Reiser, J.-C. Chen, J. Knödtel, M. Krstic, M. Reichenbach
Design Automation for Embedded Systems (2024)
DOI: 10.1007/s10617-024-09285-z, (Scale4Edge)
Among the numerous benefits that novel RRAM devices offer over conventional memory technologies is an inherent resilience to the effects of radiation. Hence, they appear suitable for use as a memory subsystem in a computer architecture for satellites. In addition to memory devices resistant to radiation, the concept of applying protective measures dynamically promises a system with low susceptibility to errors during radiation events, while also ensuring efficient performance in the absence of radiation events. This paper presents the first RRAM-based memory subsystem for satellites with a dynamic response to radiation events. We integrate this subsystem into a computing platform that employs the same dynamic principles for its processing system and implements modules for timely detection and even prediction of radiation events. To determine which protection mechanism is optimal, we examine various approaches and simulate the probability of errors in memory. Additionally, we are studying the impact on the overall system by investigating different software algorithms and their radiation robustness requirements using a fault injection simulation. Finally, we propose a potential implementation of the dynamic RRAM-based memory subsystem that includes different levels of protection and can be used for real applications in satellites.

(18) Design and Analysis of an Adaptive Radiation Resilient RRAM Subsystem for Processing Systems in Satellites
D. Reiser, J.-C. Chen, J. Knödtel, M. Krstic, M. Reichenbach
Design Automation for Embedded Systems (2024)
DOI: 10.1007/s10617-024-09285-z, (Open 6G Hub)
Among the numerous benefits that novel RRAM devices offer over conventional memory technologies is an inherent resilience to the effects of radiation. Hence, they appear suitable for use as a memory subsystem in a computer architecture for satellites. In addition to memory devices resistant to radiation, the concept of applying protective measures dynamically promises a system with low susceptibility to errors during radiation events, while also ensuring efficient performance in the absence of radiation events. This paper presents the first RRAM-based memory subsystem for satellites with a dynamic response to radiation events. We integrate this subsystem into a computing platform that employs the same dynamic principles for its processing system and implements modules for timely detection and even prediction of radiation events. To determine which protection mechanism is optimal, we examine various approaches and simulate the probability of errors in memory. Additionally, we are studying the impact on the overall system by investigating different software algorithms and their radiation robustness requirements using a fault injection simulation. Finally, we propose a potential implementation of the dynamic RRAM-based memory subsystem that includes different levels of protection and can be used for real applications in satellites.

(19) Joint Radar and Communications: Architectures, Use Cases, Aspects of Radio Access, Signal Processing, and Hardware
V. Shatov, B. Nuss, S. Schieler, P.K. Bishoyi, L. Wimmer, M. Lübke, N. Keshtiarast, Ch. Fischer, D. Lindenschmitt, B. Geiger, R. Thomä, A. Fellan, L. Schmalen, M. Petrova, H.D. Schotten, N. Franchi
IEEE Access 12, 47888 (2024)
DOI: 10.1109/ACCESS.2024.3383771, (Open 6G Hub)
Integrated Radar and Communications (JRC) can satisfy the apparent demand for applications based on object detection, tracking, ranging, and positioning. JRC is, therefore, often seen as candidate technology for 6G mobile systems. Implementing JRC will require novel approaches in many research and engineering fields, including protocol design, digital and analog signal processing, and hardware development. The ongoing debates on JRC already include many white papers and research articles ranging in content from very specific technical problems to comprehensive bird’s eye-level reviews. This paper represents the work within the Open6GHub research project in Germany, which aims to investigate and implement potential end-to-end solutions for 6G. In this framework, we propose a consolidated vision for potential JRC architectural approaches. The subsequent discussion on integrating radar sensing with communications highlights this technology’s state-of-the-art and presents relevant opportunities and challenges.

(20) FPGA Implementation of a Fault-Tolerant Fused and Branched CNN Accelerator with Reconfigurable Capabilities
R.T. Syed, Y. Zhao, J.-C. Chen, M. Andjelkovic, M. Ulbricht, M. Krstic
IEEE Access 12, 57847 (2024)
DOI: 10.1109/ACCESS.2024.3392240, (Open 6G Hub)
The ImageNet moment was a turning point for Convolutional Neural Networks (CNNs), as it demonstrated their potential to revolutionize computer vision tasks. This triumph of CNNs has motivated solving even more complex problems involving multiple tasks from multiple data modalities. Conventionally, a single CNN accelerator has been optimized to perform just one task or multiple correlated tasks. This study presents a shared-layers approach that leverages the pattern-learning capabilities of CNNs to perform multiple uncorrelated tasks from different modalities using a single hardware accelerator. We overcame the challenge of data imbalance in multi-modal learning by synthetic data generation. We achieved an average classification accuracy above 90% on a single CNN accelerator, which would otherwise require three accelerators. Due to the reliability concerns imposed by transistor shrinking and aging, we extended the shared layers methodology and introduced a fault-tolerant CNN accelerator with reconfigurable capabilities supporting fault-tolerant (FT), high-performance (HP), and de-stress (DS) modes. FT mode provides high reliability against soft errors utilizing double/triple modular redundancy, HP mode offers peak performance of 0.979 TOPs using parallel execution, and DS mode reduces dynamic power consumption by up to 68.6% in clock-gated design and even more using a partial reconfiguration method, contributing to decelerating the aging process of the circuit. We have comprehensively evaluated two different CNN architectures (i.e., fused and branched), for three distinct tasks, in three different operating modes, based on accuracy, quantization, pruning, hardware resource utilization, power, energy, performance, and reliability.

(21) Ein Ansatz zur Optimierung konfigurierbarer fehlertoleranter Systeme
M. Ulbricht, M. Krstic
Proc. 36. ITG/GMM/GI-Workshop Testmethoden und Zuverlässigkeit von Schaltungen und Systemen (TuZ 2024), 12 (2024)
(Scale4Edge)

(22) On-Chip Cross-Layer Infrastructure to Leverage System Reliability for Aero-Space Applications
F. Vargas
Proc. 27th IEEE International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS 2024), 116 (2024)
DOI: 10.1109/DDECS60919.2024.10508909

(23) Real-Time Wideband Video Synchronization via an Analog QPSK Costas Loop in a Laboratory Demonstration of an E-Band Satellite Downlink
J. Wörmann, L. Manoliu, S. Haussmann, M. Krstic, I. Kallfass
Proc. IEEE Space Hardware and Radio Conference (SHaRC 2024), 23 (2024)
DOI: 10.1109/SHaRC59908.2024.10438501

(24) Multi-Target Vital Signs Monitoring using SIMO CW RADAR
Y. Zhao, V. Sark, N. Maletic, M. Krstic, E. Grass
Proc. iCampµs Cottbus Conference (iCCC 2024), 153 (2024)
DOI: 10.5162/iCCC2024/P12, (iCampus)

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