Social media has gained importance as a channel to influence people's behavior and decisions, affecting not only the online world but also real-life (offline) events. This is especially evident in Brazil, where platforms like Telegram have been instrumental in disseminating political content rapidly and widely. However, the potential coordinated use of Telegram for promoting specific political narratives at critical times, such as the 2022 Brazilian elections, remains an area that requires further investigation. This study aims to investigate this phenomenon, focusing on the first and second rounds of voting and the January 8th riots. To this end, we conducted a comprehensive analysis of 620,000 messages from 256 Telegram groups, focusing on the dynamics of message dissemination and user interactions. Using network backbone extraction and text analysis methods, we identified key users who may be orchestrating the distribution of content. Our findings suggest that these individuals play a central role in the network's topology, relaying messages to a broader audience on dominant topics of discussion that reflect Brazil's political landscape during this turbulent period. This study not only highlights the growing influence of messaging apps on political mobilization but also contributes to our understanding of digital communication strategies in modern electoral contexts, emphasizing the need for further research in this field.

The use of host intrusion detection systems shows promising results in detecting APT campaigns due to the use of systems logs as source data to get more information about system environment. However, dealing with the increase of logs in time while tracking the execution context is a challenge for security analysts. Therefore, this work presents backbone extraction as a crucial preprocessing step, filtering out irrelevant logs. As the logs are modeled as provenance graphs, we discard spurious edges to detect residuals with distinctive node and edge distributions that indicate security threats. By applying our methodology to state-of-the-art benchmark datasets, we observed an increase in the performance of one-class classifiers by up to 62% on F1-score and 48% on recall in the Streamspot dataset and by up to 40% on F1-score and 33% on recall in the DARPA3 THEIA dataset. Moreover, our results indicate mitigation of the dependency explosion problem and underscore the ability of our methodology to improve the detection landscape by shrinking graph sizes without losing essential aspects to characterize attacks.

A large volume of content related to claims of election fraud, often associated with hate speech and extremism, was reported on Twitter during the 2020 US election, with evidence that coordinated efforts took place to promote such content on the platform. In response, Twitter announced the suspension of thousands of user accounts allegedly involved in such actions. Motivated by these events, we here propose a novel network-based approach to uncover evidence of coordination in a set of user interactions. Our approach is designed to address the challenges incurred by the often sheer volume of noisy edges in the network (i.e., edges that are unrelated to coordination) and the effects of data sampling. To that end, it exploits the joint use of two network backbone extraction techniques, namely Disparity Filter and Neighborhood Overlap, to reveal strongly tied groups of users (here referred to as communities) exhibiting repeatedly common behavior, consistent with coordination. We employ our strategy to a large dataset of tweets related to the aforementioned fraud claims, in which users were labeled as suspended, deleted or active, according to their accounts status after the election. Our findings reveal well-structured communities, with strong evidence of coordination to promote (i.e., retweet) the aforementioned fraud claims. Moreover, many of those communities are formed not only by suspended and deleted users, but also by users who, despite exhibiting very similar sharing patterns, remained active in the platform. This observation suggests that a significant number of users who were potentially involved in the coordination efforts went unnoticed by the platform, and possibly remained actively spreading this content on the system.

It is a challenging task to ensure quality in service-oriented systems deployed in cloud computing owing to the dynamicity of its environment. Many approaches have been adopted to identify and evaluate bottlenecks and problems in performance. The most common scenario consists of distributed systems that use a workload capable of enabling clients to exploit the target system in different operational conditions. However, one requirement that tends to be overlooked is to determine how the workload is executed, as software and hardware faults can lead to its mischaracterization. In this paper, a number of problems in the workload generation have been identified and summarized. A new architecture, called PEESOS-Cloud, is proposed which allows these services to be evaluated as well as to improve the ability of the workload so that it conforms with its described characteristics. Experiments in a cloud environment were conducted to show how PEESOS-Cloud works and validate its capabilities. Our experiment also showed that the mischaracterization of the workload leads to poor results, whereas an workload-aware implementation leads to a better performance evaluation.

Using a multi-cloud storage solution requires a user to make complex decisions. Making these decisions can be a problem for regular users who are not familiar with multi-cloud storage. We propose MSSF, a Multi-cloud Storage Selection Framework to automatically select a storage dispersal strategy. MSSF formalises the selection process using a knapsack optimisation problem using integer linear programming along with a rule-based system to select a multi-cloud storage strategy that fits the user needs and requires only simple inputs from the user. Our experiments show the performance and usability aspects of our solution, making it useful in real environments.