248 Behavioural Biometrics: Using Smartphone Keyboard Activity as a Proxy for Rest-Activity Patterns

Introduction Rest-activity patterns are important aspects of healthy sleep and may be disturbed in conditions like circadian rhythm disorders, insomnia, insufficient sleep syndrome, and neurological disorders. Long-term monitoring of rest-activity patterns is typically performed with diaries or actigraphy. Here, we propose a fully unobtrusive method to obtain rest-activity patterns using smartphone keyboard activity. This study investigated whether keyboard activities from habitual smartphone use are reliable estimates of rest and activity timing compared to daily self-reports within healthy participants. Methods First-year students (n = 51) used a custom smartphone keyboard to passively and objectively measure smartphone use behaviours, and filled out the Consensus Sleep Diary for one week. The time of the last keyboard activity before a nightly absence of keystrokes, and the time of the first keyboard activity following this period were used as markers. Results Results revealed high correlations between these markers and user-reported onset and offset of resting period (r ranged 0.74 - 0.80). Linear mixed models could estimate onset and offset of resting periods with reasonable accuracy (R2 ranged 0.60 - 0.66). This indicates that smartphone keyboard activity can be used to estimate rest-activity patterns. In addition, effects of chronotype and type of day were investigated. Conclusion Implementing this monitoring method in longitudinal studies would allow for long-term monitoring of (disturbances to) rest-activity patterns, without user burden or additional costly devices. It could be particularly useful in studies amongst clinical populations with sleep-related problems, or in populations for whom disturbances in rest-activity patterns are secondary complaints, such as neurological disorders. Support (if any):

Detection of advanced web bots by combining web logs with mouse behavioural biometrics

Web bots vary in sophistication based on their purpose, ranging from simple automated scripts to advanced web bots that have a browser fingerprint, support the main browser functionalities, and exhibit a humanlike behaviour. Advanced web bots are especially appealing to malicious web bot creators, due to their browser-like fingerprint and humanlike behaviour which reduce their detectability. This work proposes a web bot detection framework that comprises two detection modules: (i) a detection module that utilises web logs, and (ii) a detection module that leverages mouse movements. The framework combines the results of each module in a novel way to capture the different temporal characteristics of the web logs and the mouse movements, as well as the spatial characteristics of the mouse movements. We assess its effectiveness on web bots of two levels of evasiveness, (a) moderate web bots that have a browser fingerprint and (b) advanced web bots that have a browser fingerprint and also exhibit a humanlike behaviour. We show that combining web logs with visitors? mouse movements is more effective and robust towards detecting advanced web bots that try to evade detection, as opposed to using only one of those approaches.

Identification of the Exclusivity of Individual’s Typing Style Using Soft Biometric Elements

Biometric is used as a main security fence in a computer system. The unique characteristics of a person can be distinguished from each other. Human’s biometrics can be categorized into three types: morphological, biological and behavioural. Morphological biometrics uses physical features for recognition. Biological biometrics used to identify user based on biological features. Behavioural biometrics such as gender, culture, height and weight can be used as an additional security measure within a system. These biometric behavioural features are also known as soft biometric. This study uses soft biometric elements (gender, culture, region of birth and educational level) in the keystroke dynamic study to distinguish typing patterns in each of these categories. The Support Vector Machine (SVM) classification method is used to perform this classification for soft biometric identification. The results of this study have shown that soft biometrics in keystroke dynamic can be used to distinguish group of individuals typing.