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Exploiting Linguistic Style as a Cognitive Biometric for Continuous Verification

This paper presents an assessment of continuous verification using linguistic style as a cognitive biometric. In stylometry, it is widely known that linguistic style is highly characteristic of authorship using representations that capture authorial style at character, lexical, syntactic, and semantic levels. In this work, we provide a contrast to previous efforts by implementing a one-class classification problem using Isolation Forests. Our approach demonstrates the usefulness of this classifier for accurately verifying the genuine user, and yields recognition accuracy exceeding 98% using very small training samples of 50 and 100-character blocks.