Keystroke Biometrics for Freely Typed Text Based on CNN model

Keystroke biometrics, as an authentication method with advantages of no extra hardware cost, easy-to-integrate and high-security, has attracted much attention in user authentication. However, a mass of researches on keystroke biometrics have focused on the fixed-text analysis, while only a few took free-text analysis into consideration. And in the field of free-text analysis, most researchers usually devote their efforts to extracting the most appropriate keystroke features on their own experience. These methods were inevitably questionable due to their strong subjectivity. In this paper we proposed a multi-user keystroke authentication scheme based on CNN model, which can automatically figure out the appropriate features for the model, adjust and optimize the model constantly to further enhance the performance of model. In the experiment on a small sample set, the performance is improved more than 10% compared with the benchmark. Our model achieves an average recognition accuracy of 92.58%, with FAR of 0.24% and FRR of 7.34%

Keystroke dynamics as a biometric

Modern computer systems rely heavily on methods of authentication and identity verification to protect sensitive data. One of the most robust protective techniques involves adding a layer of biometric analysis to other security mechanisms, as a means of establishing the identity of an individual beyond reasonable doubt. In the search for a biometric technique which is both low-cost and transparent to the end user, researchers have considered analysing the typing patterns of keyboard users to determine their characteristic timing signatures.Previous research into keystroke analysis has either required fixed performance of known keyboard input or relied on artificial tests involving the improvisation of a block of text for analysis. I is proposed that this is insufficient to determine the nature of unconstrained typing in a live computing environment. In an attempt to assess the utility of typing analysis for improving intrusion detection on computer systems, we present the notion of ‘genuinely free text’ (GFT). Through the course of this thesis, we discuss the nature of GFT and attempt to address whether it is feasible to produce a lightweight software platform for monitoring GFT keystroke biometrics, while protecting the privacy of users.The thesis documents in depth the design, development and deployment of the multigraph-based BAKER software platform, a system for collecting statistical GFT data from live environments. This software platform has enabled the collection of an extensive set of keystroke biometric data for a group of participating computer users, the analysis of which we also present here. Several supervised learning techniques were used to demonstrate that the richness of keystroke information gathered from BAKER is indeed sufficient to recommend multigraph keystroke analysis, as a means of augmenting computer security. In addition, we present a discussion of the feasibility of applying data obtained from GFT profiles in circumventing traditional static and free text analysis biometrics

Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic

This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events. In particular, the massive spread of fake news during the COVID-19 pandemic has forced governments and companies to fight against missinformation. In this context, the ability to link multiple accounts or profiles that spread such malicious content on the Internet while hiding in anonymity would enable proactive identification and blacklisting. Behavioral biometrics can be powerful tools in this fight. In this work, we have analyzed how the latest advances in keystroke biometric recognition can help to link behavioral typing patterns in experiments involving 100,000 users and more than 1 million typed sequences. Our proposed system is based on Recurrent Neural Networks adapted to the context of content de-anonymization. Assuming the challenge to link the typed content of a target user in a pool of candidate profiles, our results show that keystroke recognition can be used to reduce the list of candidate profiles by more than 90%. In addition, when keystroke is combined with auxiliary data (such as location), our system achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a background candidate list composed of 1K and 100K profiles, respectively.Comment: arXiv admin note: text overlap with arXiv:2004.0362

Strengthening e-banking security using keystroke dynamics

This paper investigates keystroke dynamics and its possible use as a tool to prevent or detect fraud in the banking industry. Given that banks are constantly on the lookout for improved methods to address the menace of fraud, the paper sets out to review keystroke dynamics, its advantages, disadvantages and potential for improving the security of e-banking systems. This paper evaluates keystroke dynamics suitability of use for enhancing security in the banking sector. Results from the literature review found that keystroke dynamics can offer impressive accuracy rates for user identification. Low costs of deployment and minimal change to users modus operandi make this technology an attractive investment for banks. The paper goes on to argue that although this behavioural biometric may not be suitable as a primary method of authentication, it can be used as a secondary or tertiary method to complement existing authentication systems