An investigation of genetic algorithm-based feature selection techniques applied to keystroke dynamics biometrics

Due to the continuous use of social networks, users can be vulnerable to online situations such as paedophilia treats. One of the ways to do the investigation of an alleged pedophile is to verify the legitimacy of the genre that it claims. One possible technique to adopt is keystroke dynamics analysis. However, this technique can extract many attributes, causing a negative impact on the accuracy of the classifier due to the presence of redundant and irrelevant attributes. Thus, this work using the wrapper approach in features selection using genetic algorithms and as KNN, SVM and Naive Bayes classifiers. Bringing as best result the SVM classifier with 90% accuracy, identifying what is most suitable for both bases

Investigating the use of feature selection techniques for gender prediction systems based on keystroke dynamics

Biometric-based solutions keep expanding with new modalities, techniques and systems being proposed every so often. However, the first ones that were used for authentication, such as handwritten signature and keystroke dynamics, continue to be relevant in our digital world, despite their analogical origin. In special, keystroke dynamics has had an increase in popularity with the advent of social networks, making the need to continue to authenticate in desktop or game-based user verification more prevalent and this became an open door to risky situations such as paedophilia, sexual abuse, harassment among others. One of the ways to combat this type of crime is to be able to verify the legitimacy of the gender of the person the user is typing with. Despite the fact that keystroke dynamics is well accepted and reliable, this technique can have far too many attributes to be analysed which can lead to the use of redundant or irrelevant information. Therefore, propose a comparative study between two features selection approaches, hybrid (filter + wrapper) and wrapper. They will be tested by using a genetic algorithm, a particle swarm optimisation, a k -NN, a SVM, and a Naive Bayes as classifiers, as well as, the Correlation and Relief filters. From the results obtained, it can be said that the two proposed hybrid approaches reduce the number of attributes, without negatively impacting the accuracy of the classification, and being less costly than the traditional PSO

An investigation of the predictability of the Brazilian three-modal hand-based behavioural biometric: a feature selection and feature-fusion approach

Abstract: New security systems, methods or techniques need to have their performance evaluated in conditions that closely resemble a real-life situation. The effectiveness with which individual identity can be predicted in different scenarios can benefit from seeking a broad base of identity evidence. Many approaches to the implementation of biometric-based identification systems are possible, and different configurations are likely to generate significantly different operational characteristics. The choice of implementational structure is, therefore, very dependent on the performance criteria, which is most important in any particular task scenario. The issue of improving performance can be addressed in many ways, but system configurations based on integrating different information sources are widely adopted in order to achieve this. Thus, understanding how each data information can influence performance is very important. The use of similar modalities may imply that we can use the same features. However, there is no indication that very similar (such as keyboard and touch keystroke dynamics, for example) basic biometrics will perform well using the same set of features. In this paper, we will evaluate the merits of using a three-modal hand-based biometric database for user prediction focusing on feature selection as the main investigation point. To the best of our knowledge, this is the first thought-out analysis of a database with three modalities that were collected from the same users, containing keyboard keystroke, touch keystroke and handwritten signature. First, we will investigate how the keystroke modalities perform, and then, we will add the signature in order to understand if there is any improvement in the results. We have used a wide range of techniques for feature selection that includes filters and wrappers (genetic algorithms), and we have validated our findings using a clustering technique