Human-in-the-Loop Approach in Lawful Interception

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We aim to further the capability of the technologies related to Lawful Intercept (LI) to match the needs of a rapidly changing and developing world. We aim to build on the current state of the art technology, which will be provided through our partner, and investigate ways that this can be improved to match the increasing demands of the digital world to ensure threats are detected without detriment to others. We will be working with our project partners SS8 to advance the current state-of-the-art techniques provided by their hardware in a virtualised environment to combine high speed probes with so-called smart probe technology. In this endeavour, we will also leverage our relationships with Palo Alto Networks, and IXIA solutions to create a robust and realistic test environment, capable of simulating traffic experienced in a real-world network situation. As a result of this project, we hope to have greatly increased our understanding of the field, and the overall capacity of the field to adapt to new changes in technology and usage patterns. Through our ties to industry and government it is another goal to use what we discover in this project to inform and produce valuable guidelines that will help this field continue to adapt and further provide these to the standards committees as well as UK based institutions. Our plan for this research is to be able to take the research concepts into the "real world" developing solutions to security problems as they become apparent. This will not only provide valuable innovation for the security sector but will also allow for development of the fellow and his team, and the company moving into a field that would not be possible without the support of the UKRI.The PhD.Year one - background research and problem understanding to which there are three key areas: human interaction with the system, state-of-the-art machine learning approaches tothis problem and familiarisation with the specific technology used by the stakeholder. Researching the challenging context and understanding the humans involved.There is human involvement at different levels of decision making. We have to first understand who the relevant people are and how they are contributing to this pipeline. What is their level of expertise? How much time and capacity do they have to interpret - or interact with the system? What types of interpretation or explanation through interaction are suitable? Forexample: inclusion of humans in the loop by providing insight and decisions to improve the algorithm's learning, this approach is less time critical and can be moved off-line. In contrastin-line interaction and interpretation is more difficult due to the very high volumes of data being processed and might focus more on directing computation time or discarding data e.g.filtering IoT traffic, focussing on certain IP addresses or types of communication all based on current intelligence. This research will lead to formalisations of approaches and designswhere the human is appropriately embedded in decision-making systems. Year two Prototyping system designs that are fast and can provide accurate insights to users through interaction. In the first instance, the student will train an offline model and compare various techniques to identify the best option form two key it is fast in producing predictions, and can be implemented in custom hardware that can work on ITSUS systems. Furthermore, this prototyping phase will serve to improve the social-technical decision making - e.g. adversary detection - through human in the loop active learning and understanding through insight interaction Supporting active learning on this massive dataset, we will need to be able to work with operators to find appropriate labels for data and verifying predictions. We will deploy an active learning framework to identify the most informative data point and get feedback on its nature from the operators.Year 3Refining evaluations and writing