https://www.research.manchester.ac.uk/portal/files/33082822/COSE_Preprint_escholar.pdf

A survey on touch dynamics authentication in mobile devices

Overview of the combination of biometric matchers

Multiple classifiers in biometrics. Part 2: Trends and challenges

Keystroke biometrics (KB) authentication systems are a less popular form of access control, although they are gaining popularity. In recent years, keystroke biometric authentication has been an active area of research due to its low cost and ease of integration with existing security systems. Various researchers have used different methods and algorithms for data collection, feature representation, classification, and performance evaluation to measure the accuracy of the system, and therefore achieved different accuracy rates. Although recently, the support vector machine is most widely used by researchers, it seems that ensemble methods and artificial neural networks yield higher accuracy. Moreover, the overall accuracy of KB is still lower than other biometric authentication systems, such as iris. The objective of this paper is to present a detailed survey of the most recent researches on keystroke dynamic authentication, the methods and algorithms used, the accuracy rate, and the shortcomings of those researches. Finally, the paper identifies some issues that need to be addressed in designing keystroke dynamic biometric systems, makes suggestions to improve the accuracy rate of KB systems, and proposes some possible future research directions.

Keystroke Biometric Systems for User Authentication

Supervised learning and pattern recognition is a crucial area of research in information retrieval, knowledge engineering, image processing, medical imaging, and intrusion detection. Numerous algorithms have been designed to address such complex application domains. Despite an enormous array of supervised classifiers, researchers are yet to recognize a robust classification mechanism that accurately and quickly classifies the target dataset, especially in the field of intrusion detection systems (IDSs). Most of the existing literature considers the accuracy and false-positive rate for assessing the performance of classification algorithms. The absence of other performance measures, such as model build time, misclassification rate, and precision, should be considered the main limitation for classifier performance evaluation. This paper’s main contribution is to analyze the current literature status in the field of network intrusion detection, highlighting the number of classifiers used, dataset size, performance outputs, inferences, and research gaps. Therefore, fifty-four state-of-the-art classifiers of various different groups, i.e., Bayes, functions, lazy, rule-based, and decision tree, have been analyzed and explored in detail, considering the sixteen most popular performance measures. This research work aims to recognize a robust classifier, which is suitable for consideration as the base learner, while designing a host-based or network-based intrusion detection system. The NSLKDD, ISCXIDS2012, and CICIDS2017 datasets have been used for training and testing purposes. Furthermore, a widespread decision-making algorithm, referred to as Techniques for Order Preference by Similarity to the Ideal Solution (TOPSIS), allocated ranks to the classifiers based on observed performance reading on the concern datasets. The J48Consolidated provided the highest accuracy of 99.868%, a misclassification rate of 0.1319%, and a Kappa value of 0.998. Therefore, this classifier has been proposed as the ideal classifier for designing IDSs.

https://www.mdpi.com/2227-7390/9/6/690/pdf

Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research

Keystroke dynamics biometrics through computers are based in the time that users need to press and hold keys and often present too small amount of information. This limitation is eliminated in the environment of mobile devices due to a variety of sensors (accelerometers, gyroscopes, pressure and finger size) can be used to acquire useful information from users. These data have been acquired within the scenario of typing a 4-digit PIN in order to analyze the possibilites of reinforcing the security of mobile devices. A database with keystroke dynamics patterns has been analysed. Data has been acquired in a constrained environment, where users must hold the phone in a fixed position, and other with the data taken in unconstrained conditions. Features as pressure, finger size, times, linear an angular acceleration are extracted and processed. Supervised classification methods are widely used in different kind of biometrics. A discussion about their use in keystroke biometrics is presented. A preprocessing of the acquired data is performed using Linear Discriminant Analysis (LDA) and a reduction of the amount of information applying Principal Components Analysis (PCA). This preprocessing enhances considerably the results obtained in classification. We conclude claiming that biometric systems through keystroke dynamics with 4-digit PIN are promising.

Supervised classification methods applied to Keystroke Dynamics through Mobile Devices

gb2sμMOD: A MUltiMODal biometric video database using visible and IR light

During the last decades, several different techniques have been proposed for computer recognition of human faces. A further step in the development of these biometrics is to implement them in portable devices, such as mobile phones. Due to this devices' features and limitations it is necessary to select, among the currently available algorithms, the one with the best performance in terms of algorithm overall elapsed time and correct identification rates. The aim of this paper is to offer a complementary study to previous works, focusing on the performance of different supervised classifiers, such as the Normal Bayesian Classifier, Neural Architectures or distance-based algorithms. In addition, we analyse all the proposed algorithms' efficiency over public face databases (ORL, FERET, NIST and the Face Recognition Data from the Essex University). Each one of these databases contains a different number of individuals and particular samples and they present variations among images from the same user (scale, pose, expression, illumination, ...). We expect to simulate many different situations which take place when dealing with face recognition on mobile phones. In order to get a complete comparison, all the proposed algorithms have been implemented and run over all the databases, using the same computer. Different parametrizations for each algorithm have also been tested. Bayesian classifiers and distance-based algorithms turn out to be the most suitable, as their parametrization is simple, the training stage is not as time consuming as others' and classification results are satisfying.

Ignacio de Mendizabal-Vazquez

Papers

Supervised classification methods applied to Keystroke Dynamics through Mobile Devices

gb2sμMOD: A MUltiMODal biometric video database using visible and IR light

A comparative survey on supervised classifiers for face recognition