We are in the midst of the shift towards the Internet of Things (IoT), where more and more (legacy) devices are connected to the Internet and communicate with each other. This paradigm shift brings new security challenges and unfortunately many current security solutions are not applicable anymore, e.g., because of a lack of clear network boundaries or resource-constrained devices. However, security plays a central role: In addition to its classical function in protecting against manipulation and fraud, it also enables novel applications and innovative business models. We propose a research program that leverages binary analysis techniques to improve the security within the IoT. We concentrate on the software level since this enables us to both analyze a given device for potential security vulnerabilities and add security features to harden the device against future attacks. More specifically, we concentrate on the firmware (i.e., the combination of persistent memory together with program code and data that powers such devices) and develop novel mechanism for binary analysis of such software. We design an intermediate language to abstract away from the concrete assembly level and this enables an analysis of many different platforms within a unified analysis framework. We transfer and extend program analysis techniques such as control-/data-flow analysis or symbolic execution and apply them to our IL. Given this novel toolset, we can analyze security properties of a given firmware image (e.g., uncovering undocumented functionality and detecting memory corruption or logical vulnerabilities,). We also explore how to harden a firmware by retrofitting security mechanisms (e.g., adding control-flow integrity or automatically eliminating unnecessary functionality). This research will deepen our fundamental understanding of binary analysis methods and apply it to a novel area as it lays the foundations of performing this analysis on the level of intermediate languages.

Mammals and birds evolved vastly different forebrains. While mammals have a cortex and can reach large brain weights, bird brains are very small and are constituted by seemingly homogeneous nuclear clusters. These glaring anatomical differences cast a dim prospect on avian cognition. However, the last years showed that several avian taxa are cognitively on par with apes and show abilities like planning, theory-of-mind, and mirror-self-recognition. How is that possible? Our inability to answer this question yet, shows that we still are far away from properly understanding the link between brain structure and cognitive functions in generic ways. To gain deeper insights into these issues, we should dare to study in depth non-standard model animals. I therefore propose to conduct a detailed neurocognitive analysis of executive cognition, memory, and consciousness in pigeons. I hypothesise that despite major macroscopic differences, the pallial cognitive network of birds is highly similar to that of mammals while their memory systems differ. To test this hypothesis, I will combine behavioural analyses, fMRI, single unit recording, and optogenetics to characterize the neural mechanisms of cognition, consciousness, and memory in pigeons. I aim to: 1) reveal the pallial network for the three executive functions: inhibition, cognitive flexibility, and working memory and will subsequently search for an avian equivalent of the default mode and executive network. 2) identify an avian pallial associative area for movement intentions. 3) examine if avian associative neurons code high dimensional event representations. 4) test if birds have evolved a non-spatial memory system that is independent of hippocampus. 5) and investigate if pigeons show signatures of consciousness akin to primates. These studies will serve a deeper understanding of the link between neural structure and cognition – a link that might be less bound to neuroanatomical specificities than hitherto assumed.

Wet particle separation is used widely in mineral processing as well as plastic recycling to separate mixtures of particulate materials into further usable fractions due to density differences. Despite its wide usage wet particle separation processes are often attributed to operational problems especially if density differences of the feed material are low. A review of the state of the art clearly indicates that numerical modelling has not yet been applied to wet separation processes due to the lack of applicable numerical schemes. On this background numerical modelling can strongly contribute towards improving the design and process parameters of wet particle separation technologies especially in the field of plastic recycling - improvements have a strong environmental impact such as reduced landfill and lower overall pollution. The proposed research consists of two parts. The first objective is the development of a novel, fully Lagrangian particle-fluid modelling framework applicable to systems of particles of complex shape in a wet environment. For modelling the particles, the Discrete Element Method (DEM) will be employed. For the fluid part, the Lagrangian Smooth Particle Hydrodynamics (SPH) will be used which enables handling free surfaces and large movements of the fluid, inherently. While unresolved fluid flow around particles is already used in mesh based methods, coupling the DEM and SPH in one computational framework is a challenging task addressed in this project. Thereby, a framework is developed for representing technical scale systems of complex shaped particles in a wet environment for the first time. In the second part of the project, the developed framework will be applied to the modelling of a wet separation process involving a sink-float drum separator for plastic recycling. Both design and operational parameters will be optimized and results exchanged towards technology improvements with interested European companies from the recycling sector.