Identifying or authenticating a computer user are necessary steps to keep systems secure on the network and to prevent fraudulent users from accessing accounts. Keystroke dynamics authentication can be used as an additional authentication method. Keystroke dynamics involves in-depth analysis of how you type on the keyboard, analysis of how long a key is pressed or the time between two consecutive keys. This field has seen a continuous growth in scientific research. In the last five years alone, about 10,000 scientific researches in this field have been published. One of the main problems facing researchers is the small number of public data sets that include how users type on the keyboard. This paper aims to provide researchers with a data set that includes how to type free text on the keyboard by 80 users. The data were collected in a single session via a web platform. The dataset contains 410,633 key-events collected in a total time interval of almost 24 hours. In similar research, most datasets are with texts written by users in English. The language in which the users wrote for this research is Romanian. This paper also provides an extensive analysis of the data set collected and presents relevant information for the analysis of the data set in future research.

User-generated passwords often pose a security risk in authentication systems. However, providing a comparative substitute poses a challenge, given the common tradeoff between security and user experience. This paper integrates cryptographic methods (both asymmetric and symmetric), steganography, and a combination of physiological and behavioral biometrics to construct a prototype for a passwordless authentication system. We demonstrate the feasibility of scalable passwordless authentication while maintaining a balance between usability and security. We employed threat modeling techniques to pinpoint the security prerequisites for the system, along with choosing appropriate cryptographic protocols. In addition, a comparative analysis is conducted, examining the security impacts of the proposed system in contrast to that of traditional password-based systems. Results from the prototype indicate that authentication is possible within a timeframe similar to passwords (within 2 seconds), without imposing additional hardware costs on users to enhance security or compromising usability. Given the scalable nature of the system design and the elimination of shared secrets, the financial and efficiency burdens associated with password resets are alleviated. Furthermore, the risk of breaches is mitigated, as there is no longer a need to store passwords and/or their hashes. Differing from prior research, our study presents a pragmatic design and prototype that deserves consideration as a viable alternative for both password-based and passwordless authentication systems.

This article focuses on developing a continuous authentication system using behavioral biometrics to recognize users accessing computing devices. The user’s distinct behavioral biometric is captured through keystroke dynamics, and reward-based reinforcement learning (RL) ideas are applied to recognize them throughout the session. The suggested system adds an extra layer of security to traditional authentication methods, forming a robust continuous authentication system that can be added to static authentication systems. The methodology involves training a RL model to detect unusual user typing patterns and flag suspicious activity. Each user has an agent trained on their historical data, which is preprocessed and used to create episodes for the agent to learn from. The environment involves fetching observations and randomly corrupting them to learn out-of-order behavior. The observation vector includes both running features and summary features. The re-ward function is binary and minimalistic. The Principal Component Analysis (PCA) model is used to encode the running features, and the Double Deep Q-Network (DDQN) algorithm with a fully connected neural network is used as the policy net. The evaluation achieved an average training accuracy and EER (equal error rate) of 94.7% and 0.0126 and test accuracy and ERR of 81.06% and 0.0323 for all users when the number of encoder features was increased. Therefore, it is concluded that by continuously learning and adapting to changing behavior patterns, this approach can provide more secure and personalized authentication, lowering the possibility of unauthorized access and cyberattacks. Overall, the use of reinforcement learning and behavioral biometrics for continuous authentication has the potential to significantly enhance security in the digital age and are effective in identifying each user.