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

Keystroke Biometrics for Freely Typed Text Based on CNN model

Keystroke biometrics, as an authentication method with advantages of no extra hardware cost, easy-to-integrate and high-security, has attracted much attention in user authentication. However, a mass of researches on keystroke biometrics have focused on the fixed-text analysis, while only a few took free-text analysis into consideration. And in the field of free-text analysis, most researchers usually devote their efforts to extracting the most appropriate keystroke features on their own experience. These methods were inevitably questionable due to their strong subjectivity. In this paper we proposed a multi-user keystroke authentication scheme based on CNN model, which can automatically figure out the appropriate features for the model, adjust and optimize the model constantly to further enhance the performance of model. In the experiment on a small sample set, the performance is improved more than 10% compared with the benchmark. Our model achieves an average recognition accuracy of 92.58%, with FAR of 0.24% and FRR of 7.34%

Keystroke and Touch-dynamics Based Authentication for Desktop and Mobile Devices

The most commonly used system on desktop computers is a simple username and password approach which assumes that only genuine users know their own credentials. Once broken, the system will accept every authentication trial using compromised credentials until the breach is detected. Mobile devices, such as smart phones and tablets, have seen an explosive increase for personal computing and internet browsing. While the primary mode of interaction in such devices is through their touch screen via gestures, the authentication procedures have been inherited from keyboard-based computers, e.g. a Personal Identification Number, or a gesture based password, etc.;This work provides contributions to advance two types of behavioral biometrics applicable to desktop and mobile computers: keystroke dynamics and touch dynamics. Keystroke dynamics relies upon the manner of typing rather than what is typed to authenticate users. Similarly, a continual touch based authentication that actively authenticates the user is a more natural alternative for mobile devices.;Within the keystroke dynamics domain, habituation refers to the evolution of user typing pattern over time. This work details the significant impact of habituation on user behavior. It offers empirical evidence of the significant impact on authentication systems attempting to identify a genuine user affected by habituation, and the effect of habituation on similarities between users and impostors. It also proposes a novel effective feature for the keystroke dynamics domain called event sequences. We show empirically that unlike features from traditional keystroke dynamics literature, event sequences are independent of typing speed. This provides a unique advantage in distinguishing between users when typing complex text.;With respect to touch dynamics, an immense variety of mobile devices are available for consumers, differing in size, aspect ratio, operating systems, hardware and software specifications to name a few. An effective touch based authentication system must be able to work with one user model across a spectrum of devices and user postures. This work uses a locally collected dataset to provide empirical evidence of the significant effect of posture, device size and manufacturer on user authentication performance. Based on the results of this strand of research, we suggest strategies to improve the performance of continual touch based authentication systems

Design and Evaluation of a Pressure Based Typing Biometric Authentication System

The design and preliminary evaluation of a pressure sensor-based typing biometrics authentication system (PBAS) is discussed in this paper. This involves the integration of pressure sensors, signal processing circuit, and data acquisition devices to generate waveforms, which when concatenated, produce a pattern for the typed password. The system generates two templates for typed passwords. First template is for the force applied on each password key pressed. The second template is for latency of the password keys. These templates are analyzed using two classifiers. Autoregressive (AR) classifier is used to authenticate the pressure template. Latency classifier is used to authenticate the latency template. Authentication is complete by matching the results of these classifiers concurrently. The proposed system has been implemented by constructing users’ database patterns which are later matched to the biometric patterns entered by each user, thereby enabling the systemto accept or reject the user. Experiments have been conducted to test the performance of the overall PBAS system and results obtained showed that this proposed system is reliable with many potential applications for computer security

Smartphone Gesture-Based Authentication

In this research, we consider the problem of authentication on a smartphone based on gestures, that is, movements of the phone. Accelerometer data from a number of subjects was collected and we analyze this data using a variety of machine learning techniques, including support vector machines (SVM) and convolutional neural networks (CNN). We analyze both the fraud rate (or false accept rate) and insult rate (or false reject rate) in each case

Digest: A Biometric Authentication Protocol in Wireless Sensor Network

Since the security of biometric information may be threatened by network attacks, presenting individual’s information without a suitable protection is not suitable for authorization. In traditional cryptographic systems, security was done using individual’s password(s) or driving some other data from primary information as secret key(s). However, encryption and decryption algorithms are slow and contain time-consuming operations for transferring data in network. Thus, it is better that we have no need to decrypt an encrypted trait of an enrolled person, and the system can encrypt the user trait with the user’s passwords and then compare the results with the enrolled persons’ encrypted data stored in database. In this chapter, by considering wireless sensor networks and authenticating server, we introduce a new concept called “digest” and deal with its efficiency in dealing with the security problem. A “digest” can be derived from any kind of information trait through which nobody can capture any information of primary biometric traits. We show that this concept leads to the increase of the accuracy and accessibility of a biometric system

Strengthen user authentication on mobile devices by using user’s touch dynamics pattern

Mobile devices, particularly the touch screen mobile devices, are increasingly used to store and access private and sensitive data or services, and this has led to an increased demand for more secure and usable security services, one of which is user authentication. Currently, mobile device authentication services mainly use a knowledge-based method, e.g. a PIN-based authentication method, and, in some cases, a fingerprint-based authentication method is also supported. The knowledge-based method is vulnerable to impersonation attacks, while the fingerprint-based method can be unreliable sometimes. To overcome these limitations and to make the authentication service more secure and reliable for touch screen mobile device users, we have investigated the use of touch dynamics biometrics as a mobile device authentication solution by designing, implementing and evaluating a touch dynamics authentication method. This paper describes the design, implementation, and evaluation of this method, the acquisition of raw touch dynamics data, the use of the raw data to obtain touch dynamics features, and the training of the features to build an authentication model for user identity verification. The evaluation results show that by integrating the touch dynamics authentication method into the PIN-based authentication method, the protection levels against impersonation attacks is greatly enhanced. For example, if a PIN is compromised, the success rate of an impersonation attempt is drastically reduced from 100% (if only a 4-digit PIN is used) to 9.9% (if both the PIN and the touch dynamics are used). © 2019, The Author(s)

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