Normalizing variations in feature vector structure in keystroke dynamics authentication systems

Abstract

Usernames and passwords stubbornly remain the most prevalent authentication mechanism. Password secrecy ensures that only genuine users are granted access. If the secret is breached, impostors gain the access too. One method of strengthening password authentication is through keystroke dynamics. Keystroke dynamics algorithms typically constrain the authentication entry to one valid sequence of key presses. In this paper, we introduce the concept of event sequences. We explore the nature of variations between multiple valid key-entry sequences and propose a scheme that effectively represents these variations. We test the efficacy of the new authentication method in distinguishing users. The experimental results show that typing proficiency of individuals is not the only determining authentication factor. We show that typing sequence variations contain sufficient discriminatory information to warrant their inclusion into user authentication methods. Based on these results, we present a novel strategy to create feature vectors for keystroke dynamics-based authentication. The proposed approach ensures that the feature vector's length and structure are related only to the length of the password, independent of its content or the order of keys pressed. This normalization of feature vector structure has multiple advantages including leveraging the discriminatory power of event sequences, faster search-and-retrieval in n-graph-based authentication systems, and simplicity. The proposed authentication scheme is applicable to both static and continual authentication systems.