| 研究実績の概要 |
We initially explored how we can embed Bioinformatics 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.
Additionally, we collect data from the Twitter environment and compare our method with the SOTA in bot detection. At a later stage we will also use another dataset as a baseline to investigate how our approach of incorporating GNNs with DNA-inspired behavioural modelling works against well-known solutions based on GNNs.
The fundamental idea of our approach is to use neighbourhood information derived from an account into question (2-hops away from it) utilising GNNs and accumulate this information with the account’s behavioural patterns, namely their digital DNA. Therefore, the use a series of different GNN architectures is needed to learn graph representations focused on the account(s) into question; their digital DNA are used as parts of their node features.
Additionally, instead of using ROBERTa to contextually analyse the timeline of the account, we utilise more targeted methods (BERTweet, TwitterRoberta) which seem to work better with content derived from Twitter.
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