Computational methods

Recent past has shown that similar to software, commodity hardware suffers from various security problems. Fixing hardware, however, takes much longer than fixing software. Consequently, it is desirable to find many issues quickly rather than one issue at a time. This is currently not possible since commodity hardware is closed and reverse engineering takes significantly long before we get to security analysis. The aim of this project is to explore novel techniques for scalable hardware reverse engineering to lower the bar for the much-needed independent security analysis of commodity hardware.

The smart manufacturing system (SMS) is one of the fourth industrial revolution cornerstones, also known as Industry 4.0. SMS enables a demand-driven, highly adaptable manufacturing process instead of a very static traditional manufacturing system that targets producing a specific product set. A typical SMS consists of heterogeneous, highly-programmable components connected over the networks. Moreover, SMS relies on the HMIs for the IO operations with the human operators. Such complexity also opens up several attack surfaces that could be exploited. Such attacks include producing defective products, increasing downtime, passing wrong movement parameters to the robotic arm to harm a human being, manipulating HMI to confuse the human operators, and many more. Some existing studies look into the different security aspects of the SMS. In this project, we want to understand the different security surfaces of smart manufacturing, much more that were previously unnoticed. 

In this project, we will investigate new models and methods to map and implement distributed applications to computation and communication resources while considering scalability, adaptability, low resource availabilities, low power and high dependability: Combine wireless multi-hop communication principles based on synchronous transmissions with long-range, low power protocols, e.g., LoRa. Investigate fault tolerant and fully distributed control mechanisms for network management like communication scheduling and bandwidth allocation. The approaches studied will be based on synchronous transmissions, analysis of distributed real-time systems and end-to-end real-time cyber-physical systems.

We will investigate the theoretical and practical challenges of using energy harvesting to power nodes distributed control systems. Combining energy sources such as temperature and vibration with battery systems and wireless links enables the placement of sensor nodes where they are needed for the best data quality, regardless of the availability of wired power or communication. In the context of the NCCR, we will jointly investigate some of the open challenges in the design of autonomous, energy-neutral automation systems. In a second phase, we will investigate suitable demonstrators and applications such as wireless sensing and control in motor control and energy systems.

State Estimation is an important tool in the Energy Management systems of electric power systems to monitor the state of the system. It takes measurements from the field and usually by the means of weighted least square optimization identifies the most likely system state. This step is necessary as measurements are subject to measurement errors. Traditionally, the resulting problem formulation includes the non-linear power flow equations. However, in previous work, we have derived a formulation that allows for a linear representation exhibiting the same accuracy but clearly superior computational performance to the non-linear approaches. In this project, we propose to explore how such linear formulation provides computationally efficient means to identify and filter false data injection (FDI) attacks. Protecting the power system against FDI attacks is of critical importance as many control functionalities rely on the reported state of the system.

This project will study formal methods for reduced complexity design and verification of embedded optimization techniques for the control of high-speed nonlinear constrained systems. We will focus on machine learning techniques for differentiable parametric optimization and their application to the control of fast, nonlinear dynamic systems with a power conversion system taken as an important exemplar case study.

Selective laser melting is an additive manufacturing technology that uses a laser to melt powdered metal into a solid structure based on a sliced 3D CAD model. Currently, such processes are only controlled by setting the process parameters and set points in advance, following multiple process optimisation trials. In this project, process parameters will be optimized with a data-driven closed loop control approach, based on combination of predictive and iterative learning control.