The project considers the economical, psychological and social effects of ransomware. Ransomware is a particular type of malware, and a new crime of extortion committed online. Malicious software gets installed through a phishing email or a drive-by download on a website. When it runs, it performs an action such as the encryption of the user's files, and asks a ransom for this action to be undone. The victim is coerced into paying through psychological manipulations which sometimes masquerade as advice. Due to the subtle ways that the technological aspects of the crime blend with - and are exploited through - various human dimensions, it has profound economic, psychological and societal impacts upon its victims, which makes its eradication all the more complicated. Law Enforcement Agencies have estimated that losses to criminals using ransomware are many millions of pounds, but the true costs may never be known because victims have shown to be particularly reluctant to report. This project sets out to answer the following questions: Why is ransomware so effective as a crime and why are so many people falling victim to it? Who is carrying out ransomware attacks? How can police agencies be assisted? What interventions are required to mitigate the impacts of ransomware? In order to do so, the project gathers data from Law Enforcement Agencies (which have agreed to closely collaborate with the project), through surveys of the general public and SMEs, and through interviews with stakeholders. The data will be analysed using script analysis, behavioural analysis, and other profiling techniques, leading to narratives regarding the criminals, the victims, and the typical ransomware scenario. Economical and behavioural models of ransomware will then be constructed and used to improve ransomware mitigation and advice, as well as support for law enforcement.

We will bring together world leaders in insect biology and neuroscience with world leaders in biorobotic modelling and computational neuroscience to create a partnership that will be transformative in understanding active learning and selective attention in insects, robots and autonomous systems in artificial intelligence (AI). By considering how brains, behaviours and the environment interact during natural animal behaviour, we will develop new algorithms and methods for rapid, robust and efficient learning for autonomous robotics and AI for dynamic real world applications. Recent advances in AI and notably in deep learning, have proven incredibly successful in creating solutions to specific complex problems (e.g. beating the best human players at Go, and driving cars through cities). But as we learn more about these approaches, their limitations are becoming more apparent. For instance, deep learning solutions typically need a great deal of computing power, extremely long training times and very large amounts of labeled training data which are simply not available for many tasks. While they are very good at solving specific tasks, they can be quite poor (and unpredictably so) at transferring this knowledge to other, closely related tasks. Finally, scientists and engineers are struggling to understand what their deep learning systems have learned and how well they have learned it. These limitations are particularly apparent when contrasted to the naturally evolved intelligence of insects. Insects certainly cannot play Go or drive cars, but they are incredibly good at doing what they have evolved to do. For instance, unlike any current AI system, ants learn how to forage effectively with limited computing power provided by their tiny brains and minimal exploration of their world. We argue this difference comes about because natural intelligence is a property of closed loop brain-body-environment interactions. Evolved innate behaviours in concert with specialised sensors and neural circuits extract and encode task-relevant information with maximal efficiency, aided by mechanisms of selective attention that focus learning on task-relevant features. This focus on behaving embodied agents is under-represented in present AI technology but offers solutions to the issues raised above, which can be realised by pursuing research in AI in its original definition: a description and emulation of biological learning and intelligence that both replicates animals' capabilities and sheds light on the biological basis of intelligence. This endeavour entails studying the workings of the brain in behaving animals as it is crucial to know how neural activity interacts with, and is shaped by, environment, body and behaviour and the interplay with selective attention. These experiments are now possible by combining recent advances in neural recordings of flies and hoverflies which can identify neural markers of selective attention, in combination with virtual reality experiments for ants; techniques pioneered by the Australian team. In combination with verification of emerging hypotheses on large-scale neural models on-board robotic platforms in the real world, an approach pioneered by the UK team, this project represents a unique and timely opportunity to transform our understanding of learning in animals and through this, learning in robots and AI systems. We will create an interdisciplinary collaborative research environment with a "virtuous cycle" of experiments, analysis and computational and robotic modelling. New findings feed forward and back around this virtuous cycle, each discipline informing the others to yield a functional understanding of how active learning and selective attention enable small-brained insects to learn a complex world. Through this understanding, we will develop ActiveAI algorithms which are efficient in learning and final network configuration, robust to real-world conditions and learn rapidly.

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