Distributed Denial of Service (DDoS) attacks impose a major challenge for today's security systems, given the variety of its implementations and the scale that the attacks can achieve. One approach for their early detection is the use of Machine Learning (ML) techniques, which create rules for classifying traffic from historical data. However, different types of data contribute unequally to the assertiveness of the trained model. The use of Feature Selection (FS) techniques as a pre-processing step allows identification of the most relevant features for the problem in question. This action reduces training time and can even improve performance when noisy variables are eliminated. The current work is based on a public dataset and the XGBoost algorithm to measure the impact of FS techniques on the DDoS attack classification problem. We consider both techniques independent of the sample labels, as well as methods that use this information to rank the variables in order of importance. We analyzed the problem from the point of view of Binary and Multiclass classification. We also created a benchmark of classification metrics and execution times. Our comparisons involved the Accuracy, Precision, Recall, and F1 Score metrics for different FS methods, in addition to training and execution time. In the results it is possible to verify for both the Binary (78% reduction of the features) and Multiclass classifiers (60% reduction of the features), that the ANOVA method proved to be the most beneficial.

The Internet of Things (IoT) enables the development of innovative applications in various domains such as healthcare, transportation, and Industry 4.0. The integration of the cloud platform's large processing and storage capacity with the ubiquitous sensing and actuation provided by the devices creates an IoT architecture that provides vast raw data. The IoT devices send the data to the cloud platform with the developed IoT applications, which can use publish-subscribe systems. However, messages with sensitive content require endto-end security. Besides that, IoT devices may present processing, memory, payload, and energy restrictions. In this sense, messages in an IoT architecture need to achieve both energy-efficiency and secure message delivery. Thus, this article's main contribution refers to a system that standardizes the publish-subscribe topic and payload used by the cloud platform and the IoT devices.Our system also provides end-to-end security while surpassing the energy-efficiency to send data than the main related works in the literature regarding the use of publish-subscribe systems in IoT.