Refine
Year of publication
- 2023 (19) (remove)
Document Type
- Conference Proceeding (11)
- Article (reviewed) (5)
- Patent (2)
- Part of a Book (1)
Conference Type
- Konferenzartikel (11)
Is part of the Bibliography
- yes (19)
Keywords
- Blockchains (2)
- certificate management (2)
- predictive maintenance (2)
- 5G mobile communication (1)
- Aurikuläre Stimulation (1)
- Authentication (1)
- Authorization (1)
- Bearings (1)
- Blockchain (1)
- Blockchain-to-Blockchain communication (1)
- Cloud computing (1)
- Data breech (1)
- Deep learning (1)
- EAP-TLS (1)
- Elektroden-Interface (1)
- Entropie (1)
- Failure analysis (1)
- Fault Classification (1)
- Field-programmable gate array (FPGA) (1)
- Heuristic algorithms (1)
- Higher Education (1)
- Hybrid system (1)
- IEC/IEEE 60802 security (1)
- IIoT (1)
- Industrial Blockchain (1)
- Interoperability (1)
- Jitter (1)
- Manufacturing automation (1)
- NETCONF security (1)
- Neural networks (1)
- OT security (1)
- PKI (1)
- PROFINET Security (1)
- Performance evaluation (1)
- Physical unclonable function (1)
- Predictive Maintenance (1)
- RUL (1)
- Radio frequency (1)
- Reinforcement learning (1)
- Scalability (1)
- Security (1)
- Smart metering (1)
- TLS (1)
- TSN security (1)
- Testbed (1)
- Time Sensitive Networking (1)
- Time Synchronization (1)
- TinyML (1)
- Trust management (1)
- Wireless IoT (1)
- accelerometer (1)
- bearing (1)
- bench-marking (1)
- credentials (1)
- cryptography (1)
- degradation stages (1)
- efficient training (1)
- embedded systems (1)
- hybrid networks (1)
- intermediate domain (1)
- lifelong learning (1)
- low-cost sensors and devices (1)
- manufacturing industries (1)
- performance (1)
- primary authentication (1)
- printed electronics (1)
- quality feedback survey and results assessment (1)
- real-time systems (1)
- remaining useful life (1)
- resource efficiency (1)
- secure communication (1)
- sensor and actuator networks (1)
- sparse backpropagation (1)
- system authenticity (1)
- temperature sensor (1)
- thinned ASIC in foil (1)
- transfer learning (1)
- unique interdisciplinary international higher education approach (1)
Institute
Open Access
- Closed (12)
- Open Access (7)
- Gold (4)
- Bronze (2)
- Diamond (1)
Die Erfindung betrifft ein Verfahren zum Maximieren der von einer analogen Entropiequelle abgeleiteten Entropie, wobei das Verfahren folgende Schritte aufweist:- Bereitstellen von Eingabedaten für die analoge Entropiequelle (2);- Erzeugen von Rückgabewerten durch die analoge Entropiequelle basierend auf den Eingabedaten (3); und- Gruppieren der Rückgabewerte, wobei das Gruppieren der Rückgabewerte ein Anwenden von Versätzen auf Rückgabewerte aufweist (4).
As industrial networks continue to expand and connect more devices and users, they face growing security challenges such as unauthorized access and data breaches. This paper delves into the crucial role of security and trust in industrial networks and how trust management systems (TMS) can mitigate malicious access to these networks.The TMS presented in this paper leverages distributed ledger technology (blockchain) to evaluate the trustworthiness of blockchain nodes, including devices and users, and make access decisions accordingly. While this approach is applicable to blockchain, it can also be extended to other areas. This approach can help prevent malicious actors from penetrating industrial networks and causing harm. The paper also presents the results of a simulation to demonstrate the behavior of the TMS and provide insights into its effectiveness.
Deep learning approaches are becoming increasingly important for the estimation of the Remaining Useful Life (RUL) of mechanical elements such as bearings. This paper proposes and evaluates a novel transfer learning-based approach for RUL estimations of different bearing types with small datasets and low sampling rates. The approach is based on an intermediate domain that abstracts features of the bearings based on their fault frequencies. The features are processed by convolutional layers. Finally, the RUL estimation is performed using a Long Short-Term Memory (LSTM) network. The transfer learning relies on a fixed-feature extraction. This novel deep learning approach successfully uses data of a low-frequency range, which is a precondition to use low-cost sensors. It is validated against the IEEE PHM 2012 Data Challenge, where it outperforms the winning approach. The results show its suitability for low-frequency sensor data and for efficient and effective transfer learning between different bearing types.
Training deep neural networks using backpropagation is very memory and computationally intensive. This makes it difficult to run on-device learning or fine-tune neural networks on tiny, embedded devices such as low-power micro-controller units (MCUs). Sparse backpropagation algorithms try to reduce the computational load of on-device learning by training only a subset of the weights and biases. Existing approaches use a static number of weights to train. A poor choice of this so-called backpropagation ratio limits either the computational gain or can lead to severe accuracy losses. In this paper we present TinyProp, the first sparse backpropagation method that dynamically adapts the back-propagation ratio during on-device training for each training step. TinyProp induces a small calculation overhead to sort the elements of the gradient, which does not significantly impact the computational gains. TinyProp works particularly well on fine-tuning trained networks on MCUs, which is a typical use case for embedded applications. For typical datasets from three datasets MNIST, DCASE2020 and CIFAR10, we are 5 times faster compared to non-sparse training with an accuracy loss of on average 1%. On average, TinyProp is 2.9 times faster than existing, static sparse backpropagation algorithms and the accuracy loss is reduced on average by 6 % compared to a typical static setting of the back-propagation ratio.
Printed electronics can add value to existing products by providing new smart functionalities, such as sensing elements over large-areas on flexible or non-conformal surfaces. Here we present a hardware concept and prototype for a thinned ASIC integrated with an inkjet-printed temperature sensor alongside in-built additional security and unique identification features. The hybrid system exploits the advantages of inkjet-printable platinum-based sensors, physically unclonable function circuits and a fluorescent particle-based coating as a tamper protection layer.
One of the most important questions about smart metering systems for the end users is their data privacy and security. Indeed, smart metering systems provide a lot of advantages for distribution system operators (DSO), but functionalities offered to users of existing smart meters are still limited and society is becoming increasingly critical. Smart metering systems are accused of interfering with personal rights and privacy, providing unclear tariff regulations which not sufficiently encourage households to manage their electricity consumption in advance. In the specific field of smart grids, data security appears to be a necessary condition for consumer confidence without which they will not be able to give their consent to the collection and use of personal data concerning them.
PROFINET Security: A Look on Selected Concepts for Secure Communication in the Automation Domain
(2023)
We provide a brief overview of the cryptographic security extensions for PROFINET, as defined and specified by PROFIBUS & PROFINET International (PI). These come in three hierarchically defined Security Classes, called Security Class 1,2 and 3. Security Class 1 provides basic security improvements with moderate implementation impact on PROFINET components. Security Classes 2 and 3, in contrast, introduce an integrated cryptographic protection of PROFINET communication. We first highlight and discuss the security features that the PROFINET specification offers for future PROFINET products. Then, as our main focus, we take a closer look at some of the technical challenges that were faced during the conceptualization and design of Security Class 2 and 3 features. In particular, we elaborate on how secure application relations between PROFINET components are established and how a disruption-free availability of a secure communication channel is guaranteed despite the need to refresh cryptographic keys regularly. The authors are members of the PI Working Group CB/PG10 Security.
Die Erfindung betrifft in einem ersten Aspekt eine Vorrichtung zur transkutanen Aufbringung eines elektrischen Stimulationsreizes auf ein Ohr. Die Vorrichtung umfasst einen Schaltungsträger, mindestens zwei Elektroden sowie eine Steuerungseinheit, wobei die Steuerungseinheit dazu konfiguriert ist, anhand von Stimulationsparametern ein elektrisches Stimulationssignal an den Elektroden zu erzeugen. Dabei ist die Vorrichtung, insbesondere eine Oberfläche des Schaltungsträgers der Vorrichtung, auf eine anatomische Form eines Ohres angepasst, sodass Elektroden auf der Oberfläche des Schaltungsträgers aufgebracht sind und ausgewählte Bereiche des Ohres kontaktieren Die Vorrichtung ist dadurch kennzeichnet, dass diese weiterhin einen Sensor zur Erkennung mindestens eines physiologischen Parameter umfasst und eine Steuerungseinheit dazu konfiguriert ist, anhand des mindestens einen physiologischen Parameters die Stimulationsparameter für den Stimulationsreiz anzupassen.In einem weiteren Aspekt betrifft die Erfindung ein Verfahren zur Herstellung der erfindungsgemäßen Vorrichtung.
The Transport Layer Security protocol is a widespread cryptographic protocol designed to provide secure communication over insecure networks by providing authenticity, integrity, and confidentiality. As a first step, in the TLS Handshake Protocol a common master secret is negotiated. In many configurations, this step makes considerable use of asymmetric cryptographic algorithms. It seems to be a prevalent assumption that the use of such asymmetric cryptographic algorithms is unsuitable for resource-constrained devices. Therefore, the work at hand analyzes the runtime performance of the TLS vl.2 session establishments on an embedded ARM Cortex-M4 platform. We measure the execution time to generate and parse session establishment messages for the client and server sides. In particular, we study the impact of different elliptic curves used for the ephemeral Diffie-Hellman key exchange and the impact of different lengths and subject public key algorithms of certification paths. Our analysis shows that the use of asymmetric cryptographic algorithms is well possible on resource-constrained devices, if carefully chosen and well implemented. This allows the use of the well-proven TLS protocol also for applications from the (Industrial) Internet of Things, including Fieldbus communication.
TSN, or Time Sensitive Networking, is becoming an essential technology for integrated networks, enabling deterministic and best effort traffic to coexist on the same infrastructure. In order to properly configure, run and secure such TSN, monitoring functionality is a must. The TSN standard already has some preparations to provide such functionality and there are different methods to choose from. We implemented different methods to measure the time synchronisation accuracy between devices as a C library and compared the measurement results. Furthermore, the library has been integrated into the ControlTSN engineering framework.