Android Based Behavioral Biometric Authentication via Multi-Modal Fusion

Because mobile devices are easily lost or stolen, continuous authentication is extremely desirable for them. Behavioral biometrics provides non-intrusive continuous authentication that has much less impact on usability than active authentication. However single-modality behavioral biometrics has proven less accurate than standard active authentication. This thesis presents a behavioral biometric system that uses multi-modal fusion with user data from touch, keyboard, and orientation sensors. Testing of ve users shows that fusion of modalities provides more accurate authentication than each individual modalities by itself. Using the BayesNet classification algorithm, fusion achieves False Acceptance Rate (FAR) and False Rejection Rate (FRR) values of 9.65% and 2% respectively, each of which is 8% lower than the closest individual modality

Effective Identity Management on Mobile Devices Using Multi-Sensor Measurements

Due to the dramatic increase in popularity of mobile devices in the past decade, sensitive user information is stored and accessed on these devices every day. Securing sensitive data stored and accessed from mobile devices, makes user-identity management a problem of paramount importance. The tension between security and usability renders the task of user-identity verification on mobile devices challenging. Meanwhile, an appropriate identity management approach is missing since most existing technologies for user-identity verification are either one-shot user verification or only work in restricted controlled environments. To solve the aforementioned problems, we investigated and sought approaches from the sensor data generated by human-mobile interactions. The data are collected from the on-board sensors, including voice data from microphone, acceleration data from accelerometer, angular acceleration data from gyroscope, magnetic force data from magnetometer, and multi-touch gesture input data from touchscreen. We studied the feasibility of extracting biometric and behaviour features from the on-board sensor data and how to efficiently employ the features extracted to perform user-identity verification on the smartphone device. Based on the experimental results of the single-sensor modalities, we further investigated how to integrate them with hardware such as fingerprint and Trust Zone to practically fulfill a usable identity management system for both local application and remote services control. User studies and on-device testing sessions were held for privacy and usability evaluation.Computer Science, Department o

GTmoPass: Two-factor Authentication on Public Displays Using Gaze-touch Passwords and Personal Mobile Devices

As public displays continue to deliver increasingly private and personalized content, there is a need to ensure that only the legitimate users can access private information in sensitive contexts. While public displays can adopt similar authentication concepts like those used on public terminals (e.g., ATMs), authentication in public is subject to a number of risks. Namely, adversaries can uncover a user's password through (1) shoulder surfing, (2) thermal attacks, or (3) smudge attacks. To address this problem we propose GTmoPass, an authentication architecture that enables Multi-factor user authentication on public displays. The first factor is a knowledge-factor: we employ a shoulder-surfing resilient multimodal scheme that combines gaze and touch input for password entry. The second factor is a possession-factor: users utilize their personal mobile devices, on which they enter the password. Credentials are securely transmitted to a server via Bluetooth beacons. We describe the implementation of GTmoPass and report on an evaluation of its usability and security, which shows that although authentication using GTmoPass is slightly slower than traditional methods, it protects against the three aforementioned threats

Transparent User Authentication For Mobile Applications

The use of smartphones in our daily lives has grown steadily, due to the combination of mobility and round-the-clock multi-connectivity. In particular, smartphones are used to perform activities, such as sending emails, transferring money via mobile Internet banking, making calls, texting, surfing the Internet, viewing documents, storing medical, confidential and personal information, shopping online and playing games. Some active applications are considered sensitive and confidential and the risks are high in the event of the loss of any sensitive data or privacy breaches. In addition, after the point of entry, using techniques such as a PIN or password, the user of the device can perform almost all tasks, of different risk levels, without having to re-authenticate periodically to re-validate the user’s identity. Furthermore, the current point-of-entry authentication mechanisms consider all the applications on a mobile device to have the same level of importance and so do not apply any further access control rules. As a result, with the rapid growth of smartphones for use in daily life, securing the sensitive data stored upon them makes authentication of paramount importance. In this research, it is argued that within a single mobile application there are different processes operating on the same data but with differing risks attached. The unauthorised disclosure or modification of mobile data has the potential to lead to a number of undesirable consequences for the user. Thus, there is no single level of risk associated with a given application and the risk level changes during use. In this context, a novel mobile applications data risk assessment model is proposed to appreciate the risk involved within an application (intra-process security). Accordingly, there is a need to suggest a method to be applied continuously and transparently (i.e., without obstructing the user’s activities) to authenticate legitimate users, which is maintained beyond point of entry, without the explicit involvement of the user. To this end, a transparent and continuous authentication mechanism provides a basis for convenient and secure re-authentication of the user. The mechanism is used to gather user data in the background without requiring any dedicated activity, by regularly and periodically checking user behaviour to provide continuous monitoring for the protection of the smartphone. In order to investigate the feasibility of the proposed system, a study involving data collected from 76 participants over a one-month period using 12 mobile applications was undertaken. A series of four experiments were conducted based upon data from one month of normal device usage. The first experiment sought to explore the intra-process (i.e., within-app) and inter-process (i.e., access-only app) access levels across different time windows. The experimental results show that this approach achieved desirable outcomes for applying a transparent authentication system at an intra-process level, with an average of 6% intrusive authentication requests. Having achieved promising experimental results, it was identified that there were some users who undertook an insufficient number of activities on the device and, therefore, achieved a high level of intrusive authentication requests. As a result, there was a need to investigate whether a specific combination of time windows would perform better with a specific type of user. To do this, the numbers of intrusive authentication requests were computed based on three usage levels (high, medium and low) at both the intra- and inter-process access levels. This approach achieved better results when compared with the first set of results: the average percentage of intrusive authentication requests was 3%, which indicates a clear enhancement. The second and third experiments investigated only the intra-process and inter-process, respectively, to examine the effect of the access level. Finally, the fourth experiment investigated the impact of specific biometric modalities on overall system performance. In this research study, a Non-Intrusive Continuous Authentication (NICA) framework was applied by utilising two security mechanisms: Alert Level (AL) and Integrity Level (IL). During specific time windows, the AL process is used to seek valid samples. If there are no samples, the identity confidence is periodically reduced by a degradation function, which is 10% of current confidence in order to save power while the mobile device is inactive. In the case of the mobile user requesting to perform a task, the IL is applied to check the legitimacy of that user. If the identity confidence level is equal to or greater than the specified risk action level, transparent access is allowed. Otherwise, an intrusive authentication request is required in order to proceed with the service. In summary, the experimental results show that this approach achieved sufficiently high results to fulfil the security obligations. The shortest time window of AL= 2 min / IL = 5 min produced an average intrusive authentication request rate of 18%, whereas the largest time window (AL= 20 min / IL = 20 min) provided 6%. Interestingly, when the participants were divided into three levels of usage, the average intrusive authentication request rate was 12% and 3% for the shortest time window (AL = 2 min / IL = 5 min) and the largest time window (AL= 20 min / IL = 20), respectively. Therefore, this approach has been demonstrated to provide transparent and continuous protection to ensure the validity of the current user by understanding the risk involved within a given application.Royal Embassy of Saudi Arabia Cultural Bureau in U

Transparent User Authentication For Mobile Applications

The use of smartphones in our daily lives has grown steadily, due to the combination of mobility and round-the-clock multi-connectivity. In particular, smartphones are used to perform activities, such as sending emails, transferring money via mobile Internet banking, making calls, texting, surfing the Internet, viewing documents, storing medical, confidential and personal information, shopping online and playing games. Some active applications are considered sensitive and confidential and the risks are high in the event of the loss of any sensitive data or privacy breaches. In addition, after the point of entry, using techniques such as a PIN or password, the user of the device can perform almost all tasks, of different risk levels, without having to re-authenticate periodically to re-validate the user’s identity. Furthermore, the current point-of-entry authentication mechanisms consider all the applications on a mobile device to have the same level of importance and so do not apply any further access control rules. As a result, with the rapid growth of smartphones for use in daily life, securing the sensitive data stored upon them makes authentication of paramount importance. In this research, it is argued that within a single mobile application there are different processes operating on the same data but with differing risks attached. The unauthorised disclosure or modification of mobile data has the potential to lead to a number of undesirable consequences for the user. Thus, there is no single level of risk associated with a given application and the risk level changes during use. In this context, a novel mobile applications data risk assessment model is proposed to appreciate the risk involved within an application (intra-process security). Accordingly, there is a need to suggest a method to be applied continuously and transparently (i.e., without obstructing the user’s activities) to authenticate legitimate users, which is maintained beyond point of entry, without the explicit involvement of the user. To this end, a transparent and continuous authentication mechanism provides a basis for convenient and secure re-authentication of the user. The mechanism is used to gather user data in the background without requiring any dedicated activity, by regularly and periodically checking user behaviour to provide continuous monitoring for the protection of the smartphone. In order to investigate the feasibility of the proposed system, a study involving data collected from 76 participants over a one-month period using 12 mobile applications was undertaken. A series of four experiments were conducted based upon data from one month of normal device usage. The first experiment sought to explore the intra-process (i.e., within-app) and inter-process (i.e., access-only app) access levels across different time windows. The experimental results show that this approach achieved desirable outcomes for applying a transparent authentication system at an intra-process level, with an average of 6% intrusive authentication requests. Having achieved promising experimental results, it was identified that there were some users who undertook an insufficient number of activities on the device and, therefore, achieved a high level of intrusive authentication requests. As a result, there was a need to investigate whether a specific combination of time windows would perform better with a specific type of user. To do this, the numbers of intrusive authentication requests were computed based on three usage levels (high, medium and low) at both the intra- and inter-process access levels. This approach achieved better results when compared with the first set of results: the average percentage of intrusive authentication requests was 3%, which indicates a clear enhancement. The second and third experiments investigated only the intra-process and inter-process, respectively, to examine the effect of the access level. Finally, the fourth experiment investigated the impact of specific biometric modalities on overall system performance. In this research study, a Non-Intrusive Continuous Authentication (NICA) framework was applied by utilising two security mechanisms: Alert Level (AL) and Integrity Level (IL). During specific time windows, the AL process is used to seek valid samples. If there are no samples, the identity confidence is periodically reduced by a degradation function, which is 10% of current confidence in order to save power while the mobile device is inactive. In the case of the mobile user requesting to perform a task, the IL is applied to check the legitimacy of that user. If the identity confidence level is equal to or greater than the specified risk action level, transparent access is allowed. Otherwise, an intrusive authentication request is required in order to proceed with the service. In summary, the experimental results show that this approach achieved sufficiently high results to fulfil the security obligations. The shortest time window of AL= 2 min / IL = 5 min produced an average intrusive authentication request rate of 18%, whereas the largest time window (AL= 20 min / IL = 20 min) provided 6%. Interestingly, when the participants were divided into three levels of usage, the average intrusive authentication request rate was 12% and 3% for the shortest time window (AL = 2 min / IL = 5 min) and the largest time window (AL= 20 min / IL = 20), respectively. Therefore, this approach has been demonstrated to provide transparent and continuous protection to ensure the validity of the current user by understanding the risk involved within a given application.Royal Embassy of Saudi Arabia Cultural Bureau in U

Data Behind Mobile Behavioural Biometrics – a Survey

Behavioural biometrics are becoming more and more popular. It is hard to find a sensor that is embedded in a mobile/wearable device, which can’t be exploited to extract behavioural biometric data. In this paper, we investigate data in behavioural biometrics and how this data is used in experiments, especially examining papers that introduce new datasets. We will not examine performance accomplished by the algorithms used since a system’s performance is enormously affected by the data used, its amount and quality. Altogether, 32 papers are examined, assessing how often they are cited, have databases published, what modality data are collected, and how the data is used. We offer a roadmap that should be taken into account when designing behavioural data collection and using collected data. We further look at the General Data Protection Regulation, and its significance to the scientific research in the field of biometrics. It is possible to conclude that there is a need for publicly available datasets with comprehensive experimental protocols, similarly established in facial recognition