Stylometry as a Reliable Method for Fallback Authentication

Over the decades with advancement in artificial intelligent systems, stylometry has proven to be crucial in authorship attribution. It is an evident that by natural development, writing styles between individuals are unique and cannot be fabricated. Stylometric analysis research has been done for author identification and there is significant progress to recognize an author based on their written texts. This paper aims to evaluate the efficiency o f u sing s tylometry a s a fall back authentication method. We proposed to detect differences between writings on the same topic provided by a set of users and tested whether these differences are enough to use for an authentication system. We observed 74% accuracy in detecting the actual authors and concluded that with additional features the accuracy can be pushed to above 90%. Moreover, we deviced a threshold for authentication of a particular user. We observed that the combination of textual features can support authenticity of the user. We also analyzed the impact of some data cleaning systems like removing stop words and punctuation marks, and how they affected the overall detection outcome.