Decision trees are one of the most popular structures for decision-making and the representation of a set of rules. However, when a rule set is represented as a decision tree, some quirks in its structure may negatively affect its performance. For example, duplicate sub-trees and rule filters, that need to be evaluated more than once, could negatively affect the efficiency. This paper presents a novel algorithm based on Shannon’s expansion, which guarantees that the same rule filter is not evaluated more than once, even if repeated in other rules. This fact increases efficiency during the evaluation process using the induced decision tree. Experiments demonstrated the viability of the proposed algorithm in processing-intensive scenarios, such as in intrusion detection and data stream analysis.@en

Persuasion is a human activity of influence. In marketing, persuasion can help customers find solutions to their problems, make informed choices, or convince someone to buy a useful (or useless) product or service. In computer crimes, persuasion can trick users into revealing sensitive information, or even performing actions that benefit attackers. Phishing is one of the most common and dangerous forms of persuasion-based attacks, as it exploits human vulnerabilities rather than technical ones. Therefore, an intelligent system capable of detecting and classifying persuasion attempts might be useful in protecting users. In this work, an approach that uses Machine Learning to analyze messages based on principles of persuasion and different data representations is presented. The aim of this research is to detect which data representation and which classification algorithm obtain the best results in detecting each principle of persuasion as a prior step to detecting phishing attacks. The results obtained indicate that among the combinations tested, there is one combination of data representation and classification algorithm that performs best. The related classification models obtained can detect the principles of persuasion at a rate that varies between 0.78 and 0.86 of AUC-ROC.@en

Every day the number of devices interacting through telecommunications networks grows resulting into an increase in the volume of data and information generated. At the same time, a growing number of information security incidents is being observed including the occurrence of unauthorized accesses, also named intrusions. As a consequence of these two developments, Information and Communications services providers require automated processes to detect and solve such intrusions, and this should done quickly in order to keep the related cybersecurity risks at acceptable levels. However, the presence of large volumes of data negatively interferes with the performance of classifiers used in intrusion detection tasks, which limits their applicability in practical cases. The research reported in this paper focuses on proposing a novel feature selection algorithm for intrusion detection scenarios. To this end, an extensive literature review was executed to first discover issues in the feature selection algorithms reported. Based on the insights obtained, the new multi-measure feature selection algorithm was designed that uses qualitative information provided by multiple feature selection measures, and reduces the dimensionality of the training data set. The algorithm proposed was next extensively tested using various data sets. It provides greater efficacy than other feature selection algorithms used for intrusion detection purposes. We finalize by providing some ideas on future research in order to further improve the algorithm.@en

Telecommunications services have become a constant in people’s lives. This has inspired fraudsters to carry out malicious activities causing economic losses to people and companies. Early detection of signs that suggest the possible occurrence of malicious activity would allow analysts to act in time and avoid unintended consequences. Modeling the behavior of users could identify when a significant change takes place. Following this idea, an algorithm for online behavior change detection in telecommunication services is proposed in this paper. The experimental results show that the new algorithm can identify behavioral changes related to unforeseen events.@en