Disaggregation von Haushaltsenergiemessdaten mit tiefen neuronalen Netzen

Die aktuell besten Ansätze zur Disaggregation von Haushaltsenergiemessdaten, die von handelsüblichen Smart Meter erfasst werden, basieren auf künstlichen neuronalen Netzen, die mit einer Deep-Learning-Methodik erstellt sind. Die Leistungsfähigkeit dieser Ansätze objektiv zu vergleichen ist allerdings schwer, da die Ansätze oft auf unterschiedlichen Datensätzen evaluiert werden, Trainingsverfahren nicht ausführlich beschrieben sind und keine einheitlichen Testmetriken verwendet werden. Erst durch die Evaluation bekannter Ansätze basierend auf einem einheitlichen Aufbau für Disaggregationsexperimente wird in dieser Arbeit deutlich, dass die Praxistauglichkeit aller Ansätze insbesondere durch die geringe Anzahl unterschiedlicher Gerätemodelle im Trainingsdatensatz beschränkt ist. Um für einen festgelegten Trainingsdatensatz den Fehler bei der Gerätelastgangsschätzung dennoch zu verringern, fokussiert sich die vorliegende Arbeit auf das Problem, dass Ansätze oftmals eindeutig falsche und unplausible Gerätelastgänge ausgeben, die von realen Geräten nicht reproduziert werden können. Dazu werden zwei verschiedene neue Ansätze untersucht, die die Plausibilität der geschätzten Lastgänge sicherstellen sollen. Zur Erzeugung von plausiblen Gerätelastgängen werden unterschiedliche Teile eines Generative Adversarial Networks (GAN) verwendet. Ein dritter Ansatz entwirft ein bestehendes Netzmodell neu und kombiniert dieses mit der U-Net-Architektur durch das Hinzufügen von Querverbindungen zwischen Netzschichten. Dies soll helfen, Detailinformationen in den Lastgängen besser zu reproduzieren. Bei der Evaluation der eigenen Ansätze mit dem gleichen Experimentenaufbau werden bei dem zweiten Ansatz häufiger realisierbare Lastgänge ausgegeben. Dabei bleibt die Disaggregationsgenauigkeit auf dem gleichen Niveau. Durch einen weiteren Austausch der beim Modelltraining verwendeten Verlustfunktion wird erreicht, dass sich alle betrachteten Bewertungsmetriken im Mittel über alle Geräte verbessern. Zudem kann bei bestimmten Geräteklassen mit der im dritten Ansatz evaluierten U-Net-Architektur eine weitere Verbesserung der Bewertungsmetriken erzielt werden

Binary Exploitation in Industrial Control Systems: Past, Present and Future

Despite being a decades-old problem, binary exploitation still remains a serious issue in computer security. It is mainly due to the prevalence of memory corruption errors in programs written with notoriously unsafe but yet indispensable programming languages like C and C++. For the past 30 years, the nip-and-tuck battle in memory between attackers and defenders has been getting more technical, versatile, and automated. With raised bar for exploitation in common information technology (IT) systems owing to hardened mitigation techniques, and with unintentionally opened doors into industrial control systems (ICS) due to the proliferation of industrial internet of things (IIoT), we argue that we will see an increased number of cyber attacks leveraging binary exploitation on ICS in the near future. However, while this topic generates a very rich and abundant body of research in common IT systems, there is a lack of systematic study targeting this topic in ICS. The present work aims at filling this gap and serves as a comprehensive walkthrough of binary exploitation in ICS. Apart from providing an analysis of the past cyber attacks leveraging binary exploitation on ICS and the ongoing attack surface transition, we give a review of the attack techniques and mitigation techniques on both general-purpose computers and embedded devices. At the end, we conclude this work by stressing the importance of network-based intrusion detection, considering the dominance of resource-constrained real-time embedded devices, low-end embedded devices in ICS, and the limited ability to deploy arbitrary defense mechanism directly on these devices

CyPhERS: A cyber-physical event reasoning system providing real-time situational awareness for attack and fault response

Cyber–physical systems (CPSs) constitute the backbone of critical infrastructures such as power grids or water distribution networks. Operating failures in these systems can cause serious risks for society. To avoid or minimize downtime, operators require real-time awareness about critical incidents. However, online event identification in CPSs is challenged by the complex interdependency of numerous physical and digital components, requiring to take cyber attacks and physical failures equally into account. The online event identification problem is further complicated through the lack of historical observations of critical but rare events, and the continuous evolution of cyber attack strategies. This work introduces and demonstrates CyPhERS, a Cyber-Physical Event Reasoning System. CyPhERS provides real-time information pertaining the occurrence, location, physical impact, and root cause of potentially critical events in CPSs, without the need for historical event observations. Key novelty of CyPhERS is the capability to generate informative and interpretable event signatures of known and unknown types of both cyber attacks and physical failures. The concept is evaluated and benchmarked on a demonstration case that comprises a multitude of attack and fault events targeting various components of a CPS. The results demonstrate that the event signatures provide relevant and inferable information on both known and unknown event types

CyPhERS: A cyber-physical event reasoning system providing real-time situational awareness for attack and fault response

Cyber-physical systems (CPSs) constitute the backbone of critical infrastructures such as power grids or water distribution networks. Operating failures in these systems can cause serious risks for society. To avoid or minimize downtime, operators require real-time awareness about critical incidents. However, online event identification in CPSs is challenged by the complex interdependency of numerous physical and digital components, requiring to take cyber attacks and physical failures equally into account. The online event identification problem is further complicated through the lack of historical observations of critical but rare events, and the continuous evolution of cyber attack strategies. This work introduces and demonstrates CyPhERS, a Cyber-Physical Event Reasoning System. CyPhERS provides real-time information pertaining the occurrence, location, physical impact, and root cause of potentially critical events in CPSs, without the need for historical event observations. Key novelty of CyPhERS is the capability to generate informative and interpretable event signatures of known and unknown types of both cyber attacks and physical failures. The concept is evaluated and benchmarked on a demonstration case that comprises a multitude of attack and fault events targeting various components of a CPS. The results demonstrate that the event signatures provide relevant and inferable information on both known and unknown event types

Modeling flexibility using artificial neural networks

The flexibility of distributed energy resources (DERs) can be modeled in various ways. Each model that can be used for creating feasible load profiles of a DER represents a potential model for the flexibility of that particular DER. Based on previous work, this paper presents generalized patterns for exploiting such models. Subsequently, the idea of using artificial neural networks in such patterns is evaluated. We studied different types and topologies of ANNs for the presented realization patterns and multiple device configurations, achieving a remarkably precise representation of the given devices in most of the cases. Overall, there was no single best ANN topology. Instead, a suitable individual topology had to be found for every pattern and device configuration. In addition to the best performing ANNs for each pattern and configuration that is presented in this paper all data from our experiments is published online. The paper is concluded with an evaluation of a classification based pattern using data of a real combined heat and power plant in a smart building

The Influence of Differential Privacy on Short Term Electric Load Forecasting

There has been a large number of contributions on privacy-preserving smart metering with Differential Privacy, addressing questions from actual enforcement at the smart meter to billing at the energy provider. However, exploitation is mostly limited to application of cryptographic security means between smart meters and energy providers. We illustrate along the use case of privacy preserving load forecasting that Differential Privacy is indeed a valuable addition that unlocks novel information flows for optimization. We show that (i) there are large differences in utility along three selected forecasting methods, (ii) energy providers can enjoy good utility especially under the linear regression benchmark model, and (iii) households can participate in privacy preserving load forecasting with an individual re-identification risk < 60%, only 10% over random guessing.Comment: This is a pre-print of an article submitted to Springer Open Journal "Energy Informatics

The influence of differential privacy on short term electric load forecasting

There has been a large number of contributions on privacy-preserving smart metering with Differential Privacy, addressing questions from actual enforcement at the smart meter to billing at the energy provider. However, exploitation is mostly limited to application of cryptographic security means between smart meters and energy providers. We illustrate along the use case of privacy preserving load forecasting that Differential Privacy is indeed a valuable addition that unlocks novel information flows for optimization. We show that (i) there are large differences in utility along three selected forecasting methods, (ii) energy providers can enjoy good utility especially under the linear regression benchmark model, and (iii) households can participate in privacy preserving load forecasting with an individual membership inference risk <60%, only 10% over random guessing

Soil carbon, nitrogen and phosphorus ecological stoichiometry shifts with tree species in subalpine plantations

Understanding ecological stoichiometric characteristics of soil nutrient elements, such as carbon (C), nitrogen (N) and phosphorus (P) is crucial to guide ecological restoration of plantations in ecologically vulnerable areas, such as alpine and subalpine regions. However, there has been only a few related studies, and thus whether and how different tree species would affect soil C:N:P ecological stoichiometry remains unclear. We compared soil C:N:P ecological stoichiometry of Pinus tabulaeformis, Larix kaempferi and Cercidiphyllum japonicum to primary shrubland in a subalpine region. We observed strong tree-specific and depth-dependent effects on soil C:N:P stoichiometry in subalpine plantations. In general, the C:N, C:P and N:P of topsoil (0–10 cm) are higher than subsoil (>10 cm) layer at 0–30 cm depth profiles. The differences in C:N, N:P and C:P at the topsoil across target tree species were significantly linked to standing litter stock, tree biomass/total aboveground biomass and Margalef’s index of plant community, respectively, whereas the observed variations of C:N, N:P and C:P ratio among soil profiles are closely related to differences in soil bulk density, soil moisture, the quantity and quality of aboveground litter inputs as well as underground fine root across plantations examined. Our results highlight that soil nutrients in plantation depend on litter quantity and quality of selected tree species as well as soil physical attributes. Therefore, matching site with trees is crucial to enhance ecological functioning in degraded regions resulting from human activity