641 research outputs found
Strengthening e-banking security using keystroke dynamics
This paper investigates keystroke dynamics and its possible use as a tool to prevent or detect fraud in the banking industry. Given that banks are constantly on the lookout for improved methods to address the menace of fraud, the paper sets out to review keystroke dynamics, its advantages, disadvantages and potential for improving the security of e-banking systems. This paper evaluates keystroke dynamics suitability of use for enhancing security in the banking sector. Results from the literature review found that keystroke dynamics can offer impressive accuracy rates for user identification. Low costs of deployment and minimal change to users modus operandi make this technology an attractive investment for banks. The paper goes on to argue that although this behavioural biometric may not be suitable as a primary method of authentication, it can be used as a secondary or tertiary method to complement existing authentication systems
User Authentication and Supervision in Networked Systems
This thesis considers the problem of user authentication and supervision in networked
systems. The issue of user authentication is one of on-going concern in modem IT systems
with the increased use of computer systems to store and provide access to sensitive
information resources. While the traditional username/password login combination can be
used to protect access to resources (when used appropriately), users often compromise the
security that these methods can provide. While alternative (and often more secure)
systems are available, these alternatives usually require expensive hardware to be
purchased and integrated into IT systems. Even if alternatives are available (and
financially viable), they frequently require users to authenticate in an intrusive manner (e.g.
forcing a user to use a biometric technique relying on fingerprint recognition). Assuming
an acceptable form of authentication is available, this still does not address the problem of
on-going confidence in the users’ identity - i.e. once the user has logged in at the
beginning of a session, there is usually no further confirmation of the users' identity until
they logout or lock the session in which they are operating. Hence there is a significant
requirement to not only improve login authentication but to also introduce the concept of
continuous user supervision.
Before attempting to implement a solution to the problems outlined above, a range of
currently available user authentication methods are identified and evaluated. This is
followed by a survey conducted to evaluate user attitudes and opinions relating to login
and continuous authentication. The results reinforce perceptions regarding the weaknesses
of the traditional username/password combination, and suggest that alternative techniques
can be acceptable. This provides justification for the work described in the latter part o f
the thesis.
A number of small-scale trials are conducted to investigate alternative authentication
techniques, using ImagePIN's and associative/cognitive questions. While these techniques
are of an intrusive nature, they offer potential improvements as either initial login
authentication methods or, as a challenge during a session to confirm the identity of the
logged-in user.
A potential solution to the problem of continuous user authentication is presented through
the design and implementation o f a system to monitor user activity throughout a logged-in
session. The effectiveness of this system is evaluated through a series of trials
investigating the use of keystroke analysis using digraph, trigraph and keyword-based
metrics (with the latter two methods representing novel approaches to the analysis of
keystroke data). The initial trials demonstrate the viability of these techniques, whereas
later trials are used to demonstrate the potential for a composite approach. The final trial
described in this thesis was conducted over a three-month period with 35 trial participants
and resulted in over five million samples. Due to the scope, duration, and the volume of
data collected, this trial provides a significant contribution to the domain, with the use of a
composite analysis method representing entirely new work. The results of these trials
show that the technique of keystroke analysis is one that can be effective for the majority
of users. Finally, a prototype composite authentication and response system is presented,
which demonstrates how transparent, non-intrusive, continuous user authentication can be
achieved
Continuous and transparent multimodal authentication: reviewing the state of the art
Individuals, businesses and governments undertake an ever-growing range of activities online and via various Internet-enabled digital devices. Unfortunately, these activities, services, information and devices are the targets of cybercrimes. Verifying the user legitimacy to use/access a digital device or service has become of the utmost importance. Authentication is the frontline countermeasure of ensuring only the authorized user is granted access; however, it has historically suffered from a range of issues related to the security and usability of the approaches. They are also still mostly functioning at the point of entry and those performing sort of re-authentication executing it in an intrusive manner. Thus, it is apparent that a more innovative, convenient and secure user authentication solution is vital. This paper reviews the authentication methods along with the current use of authentication technologies, aiming at developing a current state-of-the-art and identifying the open problems to be tackled and available solutions to be adopted. It also investigates whether these authentication technologies have the capability to fill the gap between high security and user satisfaction. This is followed by a literature review of the existing research on continuous and transparent multimodal authentication. It concludes that providing users with adequate protection and convenience requires innovative robust authentication mechanisms to be utilized in a universal level. Ultimately, a potential federated biometric authentication solution is presented; however it needs to be developed and extensively evaluated, thus operating in a transparent, continuous and user-friendly manner
Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning
Authentication of smartphone users is important because a lot of sensitive
data is stored in the smartphone and the smartphone is also used to access
various cloud data and services. However, smartphones are easily stolen or
co-opted by an attacker. Beyond the initial login, it is highly desirable to
re-authenticate end-users who are continuing to access security-critical
services and data. Hence, this paper proposes a novel authentication system for
implicit, continuous authentication of the smartphone user based on behavioral
characteristics, by leveraging the sensors already ubiquitously built into
smartphones. We propose novel context-based authentication models to
differentiate the legitimate smartphone owner versus other users. We
systematically show how to achieve high authentication accuracy with different
design alternatives in sensor and feature selection, machine learning
techniques, context detection and multiple devices. Our system can achieve
excellent authentication performance with 98.1% accuracy with negligible system
overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable
Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap
with arXiv:1703.0352
A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication
Authentication is a way to enable an individual to be uniquely identified usually based on passwords and personal identification number (PIN). The main problems of such authentication techniques are the unwillingness of the users to remember long and challenging combinations of numbers, letters, and symbols that can be lost, forged, stolen, or forgotten. In this paper, we investigate the current advances in the use of behavioral-based biometrics for user authentication. The application of behavioral-based biometric authentication basically contains three major modules, namely, data capture, feature extraction, and classifier. This application is focusing on extracting the behavioral features related to the user and using these features for authentication measure. The objective is to determine the classifier techniques that mostly are used for data analysis during authentication process. From the comparison, we anticipate to discover the gap for improving the performance of behavioral-based biometric authentication. Additionally, we highlight the set of classifier techniques that are best performing for behavioral-based biometric authentication
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