Malware presents a growing problem in a world that is increasingly connected to, and reliant on, the internet. The growing, devastating potential of cyber attacks such as DDoS attacks on society and economy is largely the result of a new class of devices, the Internet of Things (IoT), whose characteristic vulnerabilities make them easy targets to be compromised and controlled by malicious actors. This study employs a mixed-method research design to examine end-users' perceptions of the security of internet connected devices, their motivations for (non-)adoption of centralized DNS-based malware mitigation measures, and the efficacy of such services in mitigating malicious activity in a real-life environment. The results indicate that centralized DNS-based malware mitigation have significant potential in reducing end-user vulnerability to malware threats, but their adoption is hindered by lacking ability to assess threats and the value and efficacy of security measures.

Scientific advances in the field of artificial intelligence, and the ever increasing processing power of computers, have opened up opportunities to use artificial in- telligence for new industries and applications. Blockrise foresaw opportunities in using artificial intelligence for digital asset management. Blockrise’s founders and the development team formalised the problem and drew up a project pro- posal, which was accepted by the TU Delft Bachelor End Project coordinators and overseen by Prof. Dr. Jan Rellermeyer in the role of TU Coach.
The development team first reserved roughly two weeks to spend on research- ing the problem and possible solutions. A research proposal was formalised, in which details of the problem were explained and abstract solutions were given. During the next six weeks, the team started the concrete implementation of the solution. The team created a neural network to use as core functionality in the product. Supporting functionality was simultaneously developed to allow re- trieval and processing of necessary data. With enough useful data on hand, the neural network could be trained to make predictions based on an asset’s closing price, opening price, the highest price and the lowest price for each next minute, hour and day.
Extensions to the product were made in the form live-data processing, and validation and visualisation of predictions. Trading strategies were included to allow fully automated decision on placing market orders. The final stage of the development period was spent tweaking the neural network parameters in order to minimise prediction error.