About the project

Objective
The goal of the collaborative impact project is to leverage the novel technical and theoretical research results from the data-limited learning project to create added value and technical transfer to the Swedish industry (Saab and AstraZeneca) and to create interactive physical demonstrators for the Digital Futures Hub and the general public.

Background
This impact project is an extension to the 4-year project Data-Limited Learning of Complex Dynamical Systems (DLL), where the new project focuses on impact activities, software prototypes, and demonstrators. See the following for the previous DLL project.

The previous DLL project consisted of three connected sub-projects (i) bioprocessing, (ii) reinforcement learning for cyber-physical systems, and (iii) theoretical foundation. Within these sub-projects, our project has resulted in significant research results. In this new collaborative impact project, we focus on a subset of these results within the three tasks. The key aspect of the usefulness of this new project is to take the results from a theoretical or pure academic setting, to create demonstrators for the public, and to enable technical transfer to the Swedish industry.

Cross-disciplinary collaboration
This project involves co-PIs and researchers from different disciplines, including computer science, automatic control, machine learning, and biotechnology. The project is divided into three main project tasks, each aiming for separate impact activities:

Objective
With the integration of information and communications technology and intelligent electric devices, substation automation systems (SAS) greatly boost the efficiency of power system monitoring and control. However, substations also bring new vulnerabilities at the frontier of a bulk power system’s wide-area monitoring and control infrastructure. They are known to be attractive targets for attackers. In this project, we will research, develop, and validate algorithms that defend against cyberattacks that aim to disrupt substation operations by maliciously changing measurements and/or spoofing spurious control commands.

We propose multiple use-inspired AI innovations that crucially leverage concurrent capabilities of SAS to transform the cyber security of power systems, including (i) a framework that synergizes optimization-based attack modelling with inverse reinforcement learning for multi-stage attack detection, (ii) a decision-focused distributed CPS modelling approach, and (iii) a mathematical program with equilibrium constraints framework of adversarial unlearning for spoofing detection.

Background
In the IEC 61850-based Substation Automation System (SAS), integrating computing and communication technologies with Intelligent Electric Devices (IEDs) greatly enhances the efficiency of power system monitoring and control. The fast-growing connectivity via wide area networks (WAN) enables powerful automation functions but also brings cyber vulnerabilities concerning new attack vectors. The substations are known to be attractive targets for attackers since they form the frontier of the wide-area monitoring and control infrastructure of a bulk power system, which consists of a Supervisory Control And Data Acquisition (SCADA) system, an Energy Management System (EMS), and a control centre.

Cyberattacks at SASs may be performed by maliciously changing measurements from IEDs and merging units (MUs) and/or spoofing spurious control commands for one or more switching devices from IEDs. An attack can alter a device’s configuration even if commands and data comply with syntax, protocol, and the targeted device. The vulnerabilities of the modern grid are many, as described in a National Academies Report.

Crossdisciplinary collaboration
Anomaly detection can reduce cyber threats to substations and improve root cause analysis. Traditional anomaly data detection heavily relies on human experts to design rule-based detection mechanisms, which can be time-consuming, inefficient, less adaptive, and labour-intensive. More recently, sophisticated anomaly detection methods have been reported in the literature. Still, they largely ignore the special characteristics of attacks on SAS and practical system-level constraints on communication and computation.

Transformative and disruptive applications of use-inspired AI for SAS anomaly detection are in their infancy. The proposed project is among the first known efforts to develop and demonstrate AI-enabled SAS anomaly data detection that crucially leverages the cross-disciplinary collaboration between substation Information engineering and Communications Technology (especially distributed machine learning) for cyber defence.

The project is a collaboration between the University of California Berkeley, Virginia Tech and KTH Royal Institute of Technology.

About the project

Objective
Soil carbon sequestration in croplands has tremendous potential to help mitigate climate change; however, it is challenging to develop optimal management practices to maximise the sequestered carbon and crop yield. This project aims to develop an intelligent agricultural management system using deep reinforcement learning (RL) and large-scale soil and crop simulations. To achieve this, we propose to build a simulator to model and simulate the complex soil-water-plant-atmosphere interactions, which will run on high-performance computing platforms.

Background
Massive simulations using such platforms allow the evaluation of the effects of various management practices under different weather and soil conditions in a timely and cost-effective manner. By formulating the management decision as an RL problem, we can leverage the state-of-the-art algorithms to train management policies, which are expected to maximise the stored organic carbon while maximising the crop yield. The whole system will be tested using data on soil and crops in both the mid-west of the United States and the Mediterranean region. The proposed research has great potential to impact climate change and food security, two of humanity’s most significant challenges.

Crossdisciplinary collaboration
This project is a collaboration between the University of Illinois at Urbana-Champaign, KTH Department of Sustainable Development and Stockholm University, Department of Physical Geography.

About the project

Objective
In this project, Princeton University and KTH Royal Institute of Technology plan to develop a family of C3.ai-enabled methods for learning, optimization, and system stability analysis of grid-tied inverters and power electronics-based power systems. The goal is to develop a unified machine-learning platform for power electronics, power systems, and data science research. A bottom-up approach, from modelling a single inverter to modelling a cluster of inverters connected as a microgrid, will be used as a motivating case study to show a holistic hierarchical modelling approach supported by C3.ai.

Background
Distributed and renewable energy resources are massively deployed in electric power grids, driven by the sharp cost reduction and the demand for carbon neutrality. The grid of the future will be supported by clouds of distributed and renewable energy resources. Power electronics Inverters are pervasively needed to connect renewable energy resources to the grid, thanks to their full controllability over electric power. On the other hand, to meet a wide range of grid requirements, these grid-tied inverters are commonly equipped with sophisticated control systems, which pose challenges to the stability and control of power grids, threatening the energy security of modern society.

Crossdisciplinary collaboration
This project is a collaboration between KTH and Princeton University.

Objective
We propose a solution using machine learning and test generation, leveraging machine learning expertise from UIUC and testing and verification from KTH. Unlike previous approaches, we focus on explainable AI in our safety cage so that the cage itself and its effects on network traffic can be inspected and validated. Lightweight approaches guarantee that our safety cage can be embedded in programmable networks or operating system kernels. Machine learning will learn behavioural models that have their roots in formal modelling (access policies, protocol states, Petri Nets) and thus are inherently readable by humans. The test-case generation will validate diverse traces against the model and showcase potential malicious behaviour, validating both positive and negative outcomes.

Background
Industrial robots usually operate within a “safety cage” to ensure that a robot does not harm workers. We need the same type of security, simple and explainable, for IT systems. Novel mechanisms that can be embedded in the network, such as through hardware-accelerated programmable networks or kernel extensions, enable this type of security at the network level.

Crossdisciplinary collaboration
The project is a collaboration between the University of Illinois at Urbana-Champaign and the KTH Royal Institute of Technology. KTH will combine its experience in testing and verification with UIUC’s expertise in machine learning.

About the project

Objective
The team will address five objectives regarding cyberattacks on power systems based on state-of-the-art AI methods: (1) designing graph neural networks that can process power data to learn the state of the system and detect cyberattacks; (2) developing AI algorithms that utilize image recognition techniques using convolutional neural networks to detect denial of view and image replays resulting from cyberattacks; and (3) developing optimization techniques to robustify previously designed neural networks against adversarial data. Selecting power system operating points and policies through attack-aware methods creates a resilient system. If an attack is not immediately sensed, operating from such a position of strength buys time for detection algorithms. Objectives 4 and 5 aim to develop attack-aware AI methods via distributionally robust optimization and cascading failure analysis.

Background
The operation of power systems is becoming data-centric to improve the efficiency, resiliency, and sustainability of power systems and address climate change. Major operational problems, such as security-constrained optimal power flow, contingency analysis, and transient stability analysis, rely on the knowledge extracted from sensory data. Data manipulation by a malicious actor tampers with grid operation, with catastrophic consequences, including physical equipment damage and cascading failures. Developing frameworks and methodologies that help power operators protect the power grid against such malicious attacks is paramount to national security.

Crossdisciplinary collaboration
The project is a collaboration between the University of California Berkeley, California Institute of Technology, KTH Royal Institute of Technology and Electric Power Research Institute. Assistant Professor Jan Kronqvist leads the research in the Department of Mathematics at KTH. At KTH, the research is focused on developing optimization techniques to robustify previously designed neural networks against adversarial data and the fundamental mathematical theory needed to develop such optimization techniques.

Contacts at other participating institutes:

Javad Lavaei, Associate Professor, Industrial Engineering and Operations Research, University of California, Berkeley
Somayeh Sojoudi, Assistant Professor of Electrical Engineering & Computer Science, University of California, Berkeley
Steven Low, Professor of Computing and Mathematical Sciences and Electrical Engineering, California Institute of Technology
Jeremy Lawrence, Principal Technical Leader at Electric Power Research Institute, Electric Power Research Institute

About the project

Objective
We propose to develop computationally efficient machine learning algorithms and tools for attack detection and identification based on a novel, scalable representation of the physical system state, the communication protocol state and the IT infrastructure’s security state maintained based on noisy observations and measurements from the physical and the IT infrastructure. The key contribution is to learn a succinct representation of the security state of the IT infrastructure that allows computationally efficient belief updates in real-time and enables jointly accounting for the evolution of the state of the physical system, communication protocols, and infrastructure for accurate detection of attacks and identification through causal reasoning based on learnt dependency models.

The research will help address questions such as achieving real-time situational awareness in complex IT infrastructures, developing anomaly detectors with low false positive and false negative rates, and using information about IT infrastructure to improve attack identification. The project leverages the expertise of three research teams from KTH, UIUC, and MIT, with extensive expertise in cyber-physical systems security, smart grids, and anomaly detection.

Background
Modern SCADA systems rely on IP-based communication protocols that are primarily event-driven and follow a publish-subscribe model. The timing and content of protocol messages emerge from interactions between the physical system state and the protocol’s internal state – as an effect, traditional approaches to anomaly detection result in excessive false positives and, ultimately, alarm fatigue.

Crossdisciplinary collaboration
The project is a collaboration between the KTH Royal Institute of Technology, the University of Illinois at Urbana-Champaign and MIT.