Continuous User Authentication Featuring Keystroke Dynamics Based on Robust Recurrent Confidence Model and Ensemble Learning Approach

Abstract

User authentication is considered to be an important aspect of any cyber security program. However, one-time validation of user's identity is not strong to provide resilient security throughout the user session. In this aspect, continuous monitoring of session is necessary to ensure that only legitimate user is accessing the system resources for entire session. In this paper, a true continuous user authentication system featuring keystroke dynamics behavioural biometric modality has been proposed and implemented which tends to authenticate the user on each action. The proposed 2-phase methodology, consisting of ensemble learning and robust recurrent confidence model, decides the genuineness of user based on each action and locks out the imposter user based on final threshold, however, it tends to lock out imposter user more quickly if it reaches the alert threshold. Moreover, system has been validated with two different experimental settings and results are reported in terms of mean average number of genuine actions (ANGA) and average number of imposter actions(ANIA), whereby achieving the lowest mean ANIA with experimental setting II.

Online Student Authentication and Proctoring System Based on Multimodal Biometrics Technology

“…Keystroke dynamics in static text requires less effort to be implemented and it also reached lower error rates in the literature [50]. However, a dynamic text analysis [51] is necessary to keep final student passiveness in the authentication process without bothering them by asking them to type a predefined paragraph (usually not related to the e-learning activities in progress). This approach considers the fact that the keystroke dynamics of one person may vary in different psychoemotional states.…”

Multi-modal biometric fusion based continuous user authentication for E-proctoring using hybrid LCNN-Salp swarm optimization

“…Kiyani et al [30] have proposed keystroke components: a predictable customer affirmation system with lead biometric structures. Every limit offers another technique for recognizing the customer, which chooses the credibility of the current customer subject to the authentic thought of every limit.…”

Keystroke Dynamics-Based Authentication Using Unique Keypad

“…Recently, several studies have applied keystroke dynamics-based authentication in various environments. These include studies based on keystroke dynamics from free text, in contrast to other work that utilized fixed length elements, such as passwords or PINs [ 10 , 11 ], as well as research that continuously classifies the user’s keystroke dynamics [ 11 , 12 , 13 ] and studies that consider various typing positions, such as sitting, walking, and relaxing positions [ 14 ].…”

“…The Manhattan distance metric is used to determine the shortest distance between two points in n-dimensions as shown in Equations ( 11) and (12). The Manhattan distance is calculated using the following Equation (13).…”