Dependable Computing – EDCC 2024 Workshops

With the increasing complexity of automated driving systems, formal verification as well as statistical verification that solely relies on real-world testing methods, become infeasible. Virtual testing seems like a promising alternative to traditional methods, especially as part of a scenario-based verification and validation methodology. But in order to transfer the test results of a system from a simulation to the real world, we need to argue the validity of the virtual tests. Our proposed method enables this validity argumentation by comparing the virtual test traces against traces that have sufficiently similar recorded real-world traces. To reduce the amount of required real-world data, the method involves two mechanisms to generalize the validity statement of a single real-world trace to a set of virtual traces. The reduction of required data is showcased in a proof of concept that compares the needed amounts of data with a “naive” validation method and here presented enhancements in an ablation study.

The level of automation in human-centered systems is steadily increasing, leading to a demand for advanced design methods for automation control at the human-machine interface. This is particularly important in safety-critical applications, where the multi-faceted interaction between the automated system and humans must be carefully analyzed to identify potential risks to the overall safety. This paper presents our vision of an approach determining an appropriate level of automation taking into account the automation’s impact on the human. The approach is based on a game theoretic framework where we investigate whether the automation’s controller can be synthesized as a strategy considering human behavior and thus ensuring human-adaptive control.

Unmanned Aerial Vehicles (UAVs) are on the rise across a wide range of application domains like shipping and delivery, precise agriculture, geographic mapping, and search and rescue. Thus, ensuring UAVs’ safe operations and reliable integration into civilian airspace is essential. These unmanned vehicles face various potential hazards and threats, such as software or hardware failures (e.g., GPS malfunctions), communication failures, and security attacks (e.g., GPS Spoofing), which can threaten mission completion and safety. Thus, implementing a fault-tolerant mechanism to improve the resilience of UAVs is crucial. This research aims to introduce a fault-tolerance mechanism employing a physics-based model that accurately estimates drone positions in the presence of hazardous conditions, particularly in the presence of GPS faults. The physics model that relies on Newton’s Second Law of Motion, enables real-time and precise estimation of the drone’s position in faulty conditions throughout a mission. Thus, the physics model’s values can replace the erroneous GPS input values. The results obtained through our experiments, conducted using fault-injection techniques in a simulated environment, demonstrate the effectiveness of our physics-based fault-tolerant mechanism, particularly in mitigating GPS-related hazards.

The safety of a system can only be demonstrated to have been achieved in a defined context. This is true whether it is a ‘traditional’ or autonomous system (AS). For traditional systems, a human is trusted to provide an oversight of operations, and react safely to unexpected scenarios that occur. For AS we cannot necessarily rely on human oversight to handle unexpected events, and must therefore be more confident that all possible hazardous scenarios are understood prior to operation. This makes the task of defining the context of safe operation (CSO) precisely and completely even more important for an AS so that unexpected scenarios can be limited. Attempting to define the CSO completely for an AS operating in a complex open-world environment could be an intractable task. It is therefore imperative that an effective and efficient way to define the CSO for AS can be found.

Existing approaches to defining the CSO for AS are generally seen to be disjoint (in that each of the elements is considered and specified in isolation) and lacking in focus (in that the level of detail is found to be inconsistent and often inappropriate). What is required therefore is a targeted, iterative and integrated approach for defining the CSO for an AS. We provide an example of how this approach can be used to deliver an effective CSO for an autonomous robot.

An assurance case has become an integral component for the certification of safety-critical systems. While manually defining assurance case patterns can be not avoided, system-specific instantiations of assurance case patterns are both costly and time-consuming. It becomes especially complex to maintain an assurance case for a system when the requirements of the System-Under-Assurance change, or an assurance claim becomes invalid due to, e.g., degradation of a systems’ component, as common when deploying learning-enabled components.

In this paper, we report on our preliminary experience leveraging the tool integration framework Evidential Tool Bus (ETB) for the construction and continuous maintenance of an assurance case from a predefined assurance case pattern. Specifically, we demonstrate the assurance process on an industrial Automated Valet Parking system from the automotive domain. We present the formalization of the provided assurance case pattern in the ETB processable logical specification language of workflows. Our findings, show that ETB is able to create and maintain evidence required for the construction of an assurance case.

Extensive testing and simulation in different environments has been suggested as one piece of evidence for the safety of autonomous systems, e.g., in the automotive domain. To enable statements on the absolute number or fractions of tested scenarios, methods and tools for computing their coverage are needed. In this paper, we present STARS, a tool for specifying semantic environment features and measuring scenario coverage when testing autonomous systems.

The role of phasor measurement unit (PMU) data as real-time indicators of system dynamics is critically important for accurate state estimation in power systems. PMUs, being cyber-physical devices, are susceptible to cyber-attacks, such as false data injection (FDI). As FDI can lead to incorrect state estimation and subsequent destructive impacts, the prompt detection of falsified data is crucial to preclude such adverse outcomes. In response to this challenge, this paper introduces a spatial-temporal graph neural network (ST-GNN) for the detection and localization of anomalies in the PMU network. The model incorporates a convolutional neural network and long short-term memory units, which are adept at extracting spatial and temporal features effectively. The inclusion of graph-based analysis in our model significantly improves the understanding of interconnections between neighboring PMUs, thereby enhancing its precision in detecting and pinpointing anomalies, even under sophisticated stealth false data injection attacks. The performance of this framework has been thoroughly evaluated on two IEEE test systems, the 39-bus and 127-bus systems, across a variety of attack scenarios. The results from these evaluations affirm the high accuracy of the model, highlighting its potential as a reliable tool for safeguarding power systems against cyber-physical threats.

To further integrate renewable energy resources into the electricity grids, increasing the consumption of locally produced electricity is one of the key solutions to reduce the operational cost of the future energy system. Therefore, the local and intelligent principles of microgrids in which users can directly exchange energy with other local users via peer-to-peer energy trading functionalities for flexible energy management are of paramount importance. Regarding peer-to-peer energy trading, setting up a virtual trading network for users can be realised via several communication and database mechanisms, in which the focus of the current work is on the application of the blockchain technology. This article aims to shed a light on the required actions and implementation efforts such as pricing mechanisms, privacy constraints, scalability and on the required overarching energy management system. By overviewing the available literature on the application of blockchain technology, this article also aims to provide a critical view on the applicability of this particular technology for peer-to-peer energy trading purposes.

Phasor measurement units (PMUs) are essential instruments in delivering real-time data crucial for monitoring the dynamics of power systems. They are widely used in transient stability prediction (TSP), significantly contributing to the effective maintenance of power systems post-contingency stability. The accuracy and reliability of data derived from PMUs are crucial for the effective execution of TSP. However, the PMU data is at risk of being compromised by false data injection (FDI) attacks. Such vulnerabilities could lead to a significant degradation in the reliability of the data, potentially resulting in the misdirection of algorithms tailored for TSP. In response to this challenge, this article presents a resilient approach for TSP capable of functioning effectively under FDI attacks. Utilizing a long short-term memory autoencoder (LSTM-AE), our proposed method is engineered to proficiently capture and learn the normative spatial and temporal correlations and patterns present in time-series PMU data, across both steady-state and transient operational states. Consequently, this approach facilitates the algorithmic reconstruction and rectification of PMU measurements that have been compromised due to FDI, thereby upholding the robustness of the TSP process in the face of cyber threats. The performance of the proposed method is validated using the IEEE 39-bus system, subjected to a wide array of scenarios. This rigorous testing demonstrates the algorithm's robustness and effectiveness in maintaining accurate TSP in scenarios where the integrity of PMU data is professionally compromised to avoid easy detection or reconstruction.

Faced with the prompt technological development of AI and blockchain technologies globally, policymakers are empowered to propose (make) laws to protect fundamental human rights following the opportunities and addressing challenges, even threats, presented by AI applications in everyday life. It aims to set future-proof and innovation-friendly standards, draft legal frameworks, develop new global norms, and harmonize landmark rules to ensure AI can be trusted: it is a force for good in society, works for people, and is not considered a clear threat to them. Democracy, the rule of law, safety and security, transparency, and trust following the protection of fundamental human rights are at stake. AI that help to manipulate human behavior to circumvent users’ free will and permit some ‘social scoring’ by governments or pro-government majority in the parliament should be banned while demonstrating potential danger, clear threat, and causing unacceptable risk. Several countries worldwide (Australia, Brazil, Canada, China, India, Japan, Korea, New Zealand, Saudi Arabia, Singapore, the United Kingdom, and the USA) have adopted a proactive approach toward AI regulation. Those countries aim to implement essential policies and infrastructure measures to cultivate a robust AI sector rather than introduce specific legislation to regulate its growth. Without comprehensive legislation, governments have published some legal frameworks, guidelines, and roadmaps, white papers that depict the future of possible AI regulation in these countries and help responsibly manage their AI usage. Finally, the European Union joined ‘the club’ following the political agreement reached recently, on December 11, 2023, between the European Parliament and the Council on the Artificial Intelligence Act (the first-ever comprehensive legal framework on AI globally), proposed by the Commission in April 2021.

This exploratory study investigates the relationship between trust and blockchain technology (BCT) in peer-to-peer (P2P) energy trading within smart grids. The research highlights the various benefits BCT brings to P2P energy trading, such as improved efficiency, cost reduction, and the optimization of renewable energy distribution. However, it also identifies significant barriers to the contribution of BCT to the removal of trust in this context. The paper explores how trust is established in blockchain systems and the paradoxical need for trust among parties for BCT adoption in energy trading, despite its trust-removing premise, due to its reliance on oracles for data collection. It also examines the blockchain trilemma and how solutions to the lack of scalability might reintroduce centralisation, affecting trust in BCT-enabled P2P energy trading. The study suggests that BCT may not be able to remove the need for trust and trusted intermediaries in P2P energy trading and calls for more qualitative research to assess the actual impact of BCT on trust in P2P energy trading and to compare traditional and blockchain-based systems in this domain.

In recent years, the safety and integrity of bridges and critical infrastructure have become a paramount concern for governments and societies worldwide. Traditional inspection methods are often time-consuming, prone to human error, and can be economically taxing. The advent of advanced technologies such as Artificial Intelligence (AI) and blockchain offers a transformative approach to inspecting and maintaining these structures. In this extended abstract we discuss the perspective and opportunities presented by integrating AI and blockchain in the inspection of bridges and critical infrastructure, emphasizing the enhancement of data integrity and the potential for these technologies to revolutionize the field.