User Study: Comparison of Picture Passwords and Current Login Approaches

In this research, we conduct a user study that compares different computer/system authentication methods. More specifically, we look into comparing regular password authentication with picture authentication. Picture authentication means selecting a sequence of pictures from a set of pictures (30). We present users with both interfaces; various metrics are tracked while the participants conduct a variety of user authentication-related tasks. Other metrics include user perception of security with such technologies

Recent advances in mobile touch screen security authentication methods: a systematic literature review

The security of the smartphone touch screen has attracted considerable attention from academics as well as industry and security experts. The maximum security of the mobile phone touch screen is necessary to protect the user’s stored information in the event of loss. Previous reviews in this research domain have focused primarily on biometrics and graphical passwords while leaving out PIN, gesture/pattern and others. In this paper, we present a comprehensive literature review of the recent advances made in mobile touch screen authentication techniques covering PIN, pattern/gesture, biometrics, graphical password and others. A new comprehensive taxonomy of the various multiple class authentication techniques is presented in order to expand the existing taxonomies on single class authentication techniques. The review reveals that the most recent studies that propose new techniques for providing maximum security to smartphone touch screen reveal multi-objective optimization problems. In addition, open research problems and promising future research directions are presented in the paper. Expert researchers can benefit from the review by gaining new insights into touch screen cyber security, and novice researchers may use this paper as a starting point of their inquir

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

Evaluating the Efficacy of Implicit Authentication Under Realistic Operating Scenarios

Smartphones contain a wealth of personal and corporate data. Several surveys have reported that about half of the smartphone owners do not configure primary authentication mechanisms (such as PINs, passwords, and fingerprint- or facial-recognition systems) on their devices to protect data due to usability concerns. In addition, primary authentication mechanisms have been subject to operating system flaws, smudge attacks, and shoulder surfing attacks. These limitations have prompted researchers to develop implicit authentication (IA), which authenticates a user by using distinctive, measurable patterns of device use that are gathered from the device users without requiring deliberate actions. Researchers have claimed that IA has desirable security and usability properties and it seems a promising candidate to mitigate the security and usability issues of primary authentication mechanisms. Our observation is that the existing evaluations of IA have a preoccupation with accuracy numbers and they have neglected the deployment, usability and security issues that are critical for its adoption. Furthermore, the existing evaluations have followed an ad-hoc approach based on synthetic datasets and weak adversarial models. To confirm our observations, we first identify a comprehensive set of evaluation criteria for IA schemes. We gather real-world datasets and evaluate diverse and prominent IA schemes to question the efficacy of existing IA schemes and to gain insight into the pitfalls of the contemporary evaluation approach to IA. Our evaluation confirms that under realistic operating conditions, several prominent IA schemes perform poorly across key evaluation metrics and thereby fail to provide adequate security. We then examine the usability and security properties of IA by carefully evaluating promising IA schemes. Our usability evaluation shows that the users like the convenience offered by IA. However, it uncovers issues due to IA's transparent operation and false rejects, which are both inherent to IA. It also suggests that detection delay and false accepts are concerns to several users. In terms of security, our evaluation based on a realistic, stronger adversarial model shows the susceptibility of highly accurate, touch input-based IA schemes to shoulder surfing attacks and attacks that train an attacker by leveraging raw touch data of victims. These findings exemplify the significance of realistic adversarial models. These critical security and usability challenges remained unidentified by the previous research efforts due to the passive involvement of human subjects (only as behavioural data sources). This emphasizes the need for rapid prototyping and deployment of IA for an active involvement of human subjects in IA research. To this end, we design, implement, evaluate and release in open source a framework, which reduces the re-engineering effort in IA research and enables deployment of IA on off-the-shelf Android devices. The existing authentication schemes available on contemporary smartphones fail to provide both usability and security. Authenticating users based on their behaviour, as suggested by the literature on IA, is a promising idea. However, this thesis concludes that several results reported in the existing IA literature are misleading due to the unrealistic evaluation conditions and several critical challenges in the IA domain need yet to be resolved. This thesis identifies these challenges and provides necessary tools and design guidelines to establish the future viability of IA

Touch-screen Behavioural Biometrics on Mobile Devices

Robust user verification on mobile devices is one of the top priorities globally from a financial security and privacy viewpoint and has led to biometric verification complementing or replacing PIN and password methods. Research has shown that behavioural biometric methods, with their promise of improved security due to inimitable nature and the lure of unintrusive, implicit, continuous verification, could define the future of privacy and cyber security in an increasingly mobile world. Considering the real-life nature of problems relating to mobility, this study aims to determine the impact of user interaction factors that affect verification performance and usability for behavioural biometric modalities on mobile devices. Building on existing work on biometric performance assessments, it asks: To what extent does the biometric performance remain stable when faced with movements or change of environment, over time and other device related factors influencing usage of mobile devices in real-life applications? Further it seeks to provide answers to: What could further improve the performance for behavioural biometric modalities? Based on a review of the literature, a series of experiments were executed to collect a dataset consisting of touch dynamics based behavioural data mirroring various real-life usage scenarios of a mobile device. Responses were analysed using various uni-modal and multi-modal frameworks. Analysis demonstrated that existing verification methods using touch modalities of swipes, signatures and keystroke dynamics adapt poorly when faced with a variety of usage scenarios and have challenges related to time persistence. The results indicate that a multi-modal solution does have a positive impact towards improving the verification performance. On this basis, it is recommended to explore alternatives in the form of dynamic, variable thresholds and smarter template selection strategy which hold promise. We believe that the evaluation results presented in this thesis will streamline development of future solutions for improving the security of behavioural-based modalities on mobile biometrics