A Model Based Framework for Testing Safety and Security in Operational Technology Environments

Todays industrial control systems consist of tightly coupled components allowing adversaries to exploit security attack surfaces from the information technology side, and, thus, also get access to automation devices residing at the operational technology level to compromise their safety functions. To identify these concerns, we propose a model-based testing approach which we consider a promising way to analyze the safety and security behavior of a system under test providing means to protect its components and to increase the quality and efficiency of the overall system. The structure of the underlying framework is divided into four parts, according to the critical factors in testing of operational technology environments. As a first step, this paper describes the ingredients of the envisioned framework. A system model allows to overview possible attack surfaces, while the foundations of testing and the recommendation of mitigation strategies will be based on process-specific safety and security standard procedures with the combination of existing vulnerability databases

The DeMaDs Open Source Modeling Framework for Power System Malfunction Detection

Modeling and simulation of electrical power systems are becoming increasingly important approaches for the development and operation of novel smart grid functionalities -- especially with regard to data-driven applications as data of certain operational states or misconfigurations can be next to impossible to obtain. The DeMaDs framework allows for the simulation and modeling of electric power grids and malfunctions therein. Furthermore, it serves as a testbed to assess the applicability of various data-driven malfunction detection methods. These include data mining techniques, traditional machine learning approaches as well as deep learning methods. The framework's capabilities and functionality are laid out here, as well as explained by the means of an illustrative example.Comment: 2023 Open Source Modelling and Simulation of Energy Systems (OSMSES

Criss-cross mapping BD+30 3639: a new kinematic analysis technique

We present a new analysis of kinematic data of the young planetary nebula BD+30 3639. The data include spectroscopic long-slit and internal proper motion measurements. In this paper we also introduce a new type of mapping of kinematic proper motion data that we name "criss-cross" mapping. It basically consists of finding all points where extended proper motion vectors cross converge. From the crossing points a map is generated which helps to interpret the kinematic data. From the criss-cross mapping of BD+30 3639, we conclude that the kinematic center is approximately 0.5 arcsec offset to the South-East from the central star. The mapping does also show evidence for a non-homologous expansion of the nebula that is consistent with a disturbance aligned with the bipolar molecular bullets.Comment: 4 pages, to appear in the proceedings of the conference "Asymmetrical Planetary Nebulae V", eds. Zijlstra, et al., editorial: Ebrar

Comparison of Data-Driven Thermal Building Models for Model Predictive Control

Energy flexible buildings in combination with demand response will play a key role in the future smart grid. To implement control strategies, which enable demand response, like model predictive control, thermal building models are necessary. Therefore, three lumped capacitance models, are compared with a k-Nearest Neighbor regression model. All models show accurate prediction results, if the operating condition of the building is similar during parameter identification or rather during training and the validation period. Parameter identification of lumped capacitance models is a time-consuming task. Especially for complex lumped capacitance models, the search space for certain parameters has to be reduced to avoid local minima. The investigated k-Nearest Neighbor algorithm has the advantage of easy implementation, very fast training and minimal effort for parameter identification in combination with accurate predictions. But its seasonal dependency is very strong, which can be easily overcome with periodically data update, as it is an instance-based learning algorithm

Manufacturing process data analysis pipelines: a requirements analysis and survey

Smart manufacturing is strongly correlated with the digitization of all manufacturing activities. This increases the amount of data available to drive productivity and profit through data-driven decision making programs. The goal of this article is to assist data engineers in designing big data analysis pipelines for manufacturing process data. Thus, this paper characterizes the requirements for process data analysis pipelines and surveys existing platforms from academic literature. The results demonstrate a stronger focus on the storage and analysis phases of pipelines than on the ingestion, communication, and visualization stages. Results also show a tendency towards custom tools for ingestion and visualization, and relational data tools for storage and analysis. Tools for handling heterogeneous data are generally well-represented throughout the pipeline. Finally, batch processing tools are more widely adopted than real-time stream processing frameworks, and most pipelines opt for a common script-based data processing approach. Based on these results, recommendations are offered for each phase of the pipeline. Document type: Articl

Efficient High-Speed WPA2 Brute Force Attacks using Scalable Low-Cost FPGA Clustering

WPA2-Personal is widely used to protect Wi-Fi networks against illicit access. While attackers typically use GPUs to speed up the discovery of weak network passwords, attacking random passwords is considered to quickly become infeasible with increasing password length. Professional attackers may thus turn to commercial high-end FPGA-based cluster solutions to significantly increase the speed of those attacks. Well known manufacturers such as Elcomsoft have succeeded in creating world\u27s fastest commercial FPGA-based WPA2 password recovery system, but since they rely on high-performance FPGAs the costs of these systems are well beyond the reach of amateurs. In this paper, we present a highly optimized low-cost FPGA cluster-based WPA-2 Personal password recovery system that can not only achieve similar performance at a cost affordable by amateurs, but in comparison our implementation would also be more than $5$ times as fast on the original hardware. Since the currently fastest system is not only significantly slower but proprietary as well, we believe that we are the first to present the internals of a highly optimized and fully pipelined FPGA WPA2 password recovery system. In addition we evaluated our approach with respect to performance and power usage and compare it to GPU-based systems

Data driven transformer level misconfiguration detection in power distribution grids

As more novel devices are integrated into the electricity grid due to the changes taking place in the energy system, ways of detecting deviations from the intended settings are needed. If misconfigurations of, for example, reactive power control curves of inverters go unnoticed, the safe and reliable operation of the power grid can no longer be ensured due to possible voltage violations or overloadings. Therefore, methods of detection of misconfigurations of said inverters using operational data at transformers are presented and compared. These methods include preprocessing by dimensionality reduction as well as detection by supervised learning approaches. The data used is of high reliability as it was collected in a lab setting reenacting typical and relevant grid operation situations. Furthermore, this data was recreated by simulation to validate the simulation data, which could also potentially be used for detection applications on a bigger scale. The results for both data sources were compared and conclusions drawn about applicability and usability for grid operators