Malicious entity detection using fine-grained DNA-inspired behavioural modelling

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We did a literature review of papers dealing with the problem of bot detection on social media networks (focused on Twitter) because the inspiration for the project was driven from this problem. It was obvious that since the project was envisioned, automated accounts on Twitter have become more sophisticated and more difficult to detect, as they are now more able to mimic human behaviour. Thus, we invested to aggregate Cyber Security with Bioinformatics. We worked towards three strands to achieve the integration of Bioinformatics principles in Cyber Security research: (1) a theoretical assessment of the complexity of a novel graphical password scheme using “k-mers”, (2) aggregation of DNA-inspired behavioural modelling within a ground-breaking field of research for social networks, and (3) envision to transfer knowledge learned from the latter direction of research to the problem of malicious software detection in mobile environments, using GNNs.We initially explored how we can embed Bioinformatics (in a theoretical level) to the problem of quantifying complexity (and security) for graphical passwords. We therefore implemented a graphical password scheme and tested its usability on mobile devices running the Android operating system. Then, we used Lothaire’s Combinatorics theory (definition of “finite word”, followed by the simple metric “complexity of a word”) with basic Bioinformatics (k-mers) and we introduced a complexity metric to quantify the proposed graphical password scheme space, aiming to identify how it compares with other graphical password schemes.