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Plenty of semiconductor devices are developed to operate with private data. To guarantee data privacy cryptographic algorithms are used, where the secrecy is based on the used keys. Theoretically, the cryptographic algorithms using keys with recommended lengths are secure. The issue is that a potential attacker can steal the devices and attack in a lab. Physical attacks are usually aimed to perturb normal operation of a device and to extract cryptographic keys, e.g. by means of fault injection (FI). One class of FI attacks exploits the sensitivity of semiconductor devices to light and are performed using a laser as the light source.
This work investigates the sensitivity of different logic and memory cells to optical Fault Injection attacks. Front-side attacks against cells manufactured in different IHP technologies were performed using two different red lasers controlled by Riscure software. To reach the repeatability of the experimental results and to increase the comparability of the results with attack results published in literature the setup parameters as well as setting parameters of the Riscure software were experimentally evaluated. Attacks were performed against inverter, NAND, NOR, flip-flop cells from standard libraries, radiation-hard flip-flops based on Junction Isolated Common Gate technique, radiation-tolerant Triple Modular Redundancy registers as well as non-volatile Resistive Random Access Memory (RRAM) cells. The results of attacks against volatile circuits were successful transient bit-set and bit-reset as well permanent stuck-at faults. The results of attacks against RRAM cells were successful in the sense that manipulation of all RRAM logic states was feasible. The faults injected during the performed experiments were repeatable and reproducible.
The goal of this work was not only to achieve successful FI but also to determine cell area(s) sensitive to laser illumination. Knowledge about areas sensitive to laser illumination can be used by designers to implement corresponding countermeasure(s) at the initial stage of chip development and is the necessary step to design appropriate countermeasures. For example, metal fillers can be applied as optical obstacles reducing the success of front-side FI attacks, i.e. as a possible low-cost countermeasure. Based on the knowledge of the sensitive cell areas, the placement of the metal fillers can be automated in the future, i.e. the findings given in the work can serve as a basis for a methodology development for improving resistance against optical FI attacks at the initial stage of chip development. Such methodology can be adapted for each chip manufacturing technology.
Die vorliegende Arbeit beschäftigt sich mit der Entwicklung eines Leitsystems für ein Microgrid.
Der Begriff Leitsystem beschreibt die Gesamtheit aller Komponenten und Einrichtungen für eine zentrale Steuerungs- und Überwachungszentrale, in der eine Datensammlung aus hierarchisch niederen Ebenen erfolgt. Das Ziel: Prozesse und Abläufe sollen erfasst und für den Anwender sichtbar gemacht werden. Als Microgrid wird in diesem Kontext ein inselnetzfähiges, lokal begrenztes Smart Grid bezeichnet.
Grundlage für die Entwicklung eines Leitsystems für ein Microgrid bildet die klassische Verteilung der Ebenen zur Anpassung standardisierter und anwendungserprobter Strukturen. Diese werden im Rahmen alternativer Ansätze abgestimmt. So wird das „Smart Grid Architecture Modell“ (SGAM) auf die Ebenen des Microgrids angewendet. Hier wird innerhalb der Ebenen nach spezifischen Aufgaben und den damit verbundenen Eigenschaften unterschieden. Das hat eine Komplexitätsreduzierung der fünf Ebenen zur Folge. Vor dem Hintergrund spezifischer Normungsaktivitäten kann so die Erarbeitung bzgl. Interoperabilitätsbedingungen innerhalb der Bereiche problemloser und übersichtlicher gehandhabt werden.
In der vorliegenden Arbeit werden verschiedene Leitsystemansätze für ein Microgrid vorgestellt. Diese können dezentrale Anlagenteile in ihren separaten Steuer- und Regelprozessen anleiten oder zum Teil beeinflussen. Ein Energiemanagementsystem mit Microgrid-relevanten Applikationen und Fahrplantools wird als Hilfsmittel zur bedarfsgerechten Führung eines intelligenten Kleinstnetzes ebenfalls abgebildet. Abschließend erfolgt die Implementierung eines Leitsystems in ein universitäres Microgrid.
Since Z3, the first automatic, programmable and operational computer, emerged in 1941, computers have become an unshakable tool in varieties of engineering researches, studies and applications. In the field of hydroinformatics, there exist a number of tools focusing on data collection and management, data analysis, numerical simulations, model coupling, post-processing, etc. in different time and space scales. However, one crucial process is still missing — filling the gap between available mass raw data and simulation tools.
In this research work, a general software framework for time series scenario composition is proposed to improve this issue. The design of this framework is aimed at facilitating simulation tasks by providing input data sets, e.g. Boundary Conditions (BCs), generated for user-specified what-if scenarios. These scenarios are based on the available raw data of different sources, such as field and laboratory measurements and simulation results. In addition, the framework also monitors the workflow by keeping track of the related metadata to ensure its traceability.
This framework is data-driven and semi-automatic. It contains four basic modules: data pre-processing, event identification, process identification, and scenario composition. These modules mainly involve Time Series Knowledge Mining (TSKM), fuzzy logic and Multivariate Adaptive Regression Splines (MARS) to extract features from the collected data and interconnect themselves. The extracted features together with other statistical information form the most fundamental elements, MetaEvents, for scenario composition and further time series generation. The MetaEvents are extracted through semi-automatic steps forming Aspects, Primitive Patterns, Successions, and Events from a set of time series raw data. Furthermore, different state variables are interconnected by the physical relationships derived from process identification. These MetaEvents represent the complementary features and consider identified physical relationships among different state variables from the available time series data of different sources rather than the isolated ones. The composed scenarios can be further converted into a set of time series data as, for example, BCs, to facilitate numerical simulations.
A software prototype of this framework was designed and implemented on top of the Java and R software technologies. The prototype together with four prototype application examples containing mathematical function-generated data, artificial model-synthetic hydrological data, and measured hydrological and hydrodynamic data, are used to demonstrate the concept. The results from the application examples present the capability of reproducing similar time series patterns from specific scenarios compared to the original ones as well as the capability of generating artificial time series data from composed scenarios based on the interest of users, such as numerical modelers. In this respect, it demonstrates the concept’s capability of answering the impacts from what-if scenarios together with simulation tools. The semi-automatic concept of the prototype also prevents from inappropriate black-box applications and allows the consideration of the knowledge and experiences of domain experts. Overall, the framework is a valuable and progressive step towards holistic hydroinformatics systems in reducing the gap between raw data and simulation tools in an engineering suitable manner.