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.

The dissemination of fake news is a problem that has already been addressed but by no means is solved. After the manipulation made by Cambridge Analytica which was based on classifying users by their political views and targeting specific political propaganda on the Brexit campaign, the Trump election and the Bolsonaro election, there is no doubt this issue can have a real impact on society in ‘normal times’. During a pandemic, any type of fake news can be the difference between life and death when the data shared can directly hurt the people who are believing in it. Moreover, there is also a new trend of using artificial robots to disseminate such news with a special target on Twitter which can be linked with political campaigns. Thus, it is essential that we identify and understand what kind of news is selected to be 'dressed' as fake and how it is disseminated. This paper aims to investigate the dissemination of fake news related with Covid-19 in the UK and Brazil in order to understand the impact of fake news on public sector actions, social isolation and quarantine imposition. Those two case studies are well versed on the fake news dissemination. Our initial dataset of Twitter posts have focused on posts from four different cities (Natal, São Paulo, Sheffield and London) and have shown interesting pointers that will be discussed.

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.

Investigating the use of feature selection techniques for gender prediction systems based on keystroke dynamics. In: 11th International Conference on Pattern Recognition Systems (ICPRS 2021). IET,[115][116][117][118][119][120]