Vaccine hesitancy was one of the ten major threats to global health in 2019, according to the World Health Organisation. Nowadays, social media has an important role in the spread of information, misinformation, and disinformation about vaccines. Monitoring vaccine-related conversations on social media could help us to identify the factors that contribute to vaccine confidence in each historical period and geographical area. We used a hybrid approach to perform an opinion-mining analysis on 1,499,227 vaccine-related tweets published on Twitter from 1st June 2011 to 30th April 2019. Our algorithm classified 69.36% of the tweets as neutral, 21.78% as positive, and 8.86% as negative. The percentage of neutral tweets showed a decreasing tendency, while the proportion of positive and negative tweets increased over time. Peaks in positive tweets were observed every April. The proportion of positive tweets was significantly higher in the middle of the week and decreased during weekends. Negative tweets followed the opposite pattern. Among users with ≥2 tweets, 91.83% had a homogeneous polarised discourse. Positive tweets were more prevalent in Switzerland (71.43%). Negative tweets were most common in the Netherlands (15.53%), Canada (11.32%), Japan (10.74%), and the United States (10.49%). Opinion mining is potentially useful to monitor online vaccine-related concerns and adapt vaccine promotion strategies accordingly.

The software development environment is focused on reaching functional products in the shortest period by making use of the least amount of resources possible. In this scenario, crucial elements such as software quality or software security are not considered at all, and in most cases, the high value offered to the projects is not taken into account. Nowadays, agile models are booming. They are defined by the way they achieve the interaction and integration of everyone involved in the software life cycle, the advantages of the quick reaction to change, and the implementation of artifacts or deliverables which display the level of progress reached at any time. In this context, it seems clearly necessary to define a new software development model, which prioritizes security aspects at any phase of the software life cycle and takes advantage of the benefits of the agile models. The proposed methodology shows that if security is considered from the beginning, vulnerabilities are easily detected and solved during the time planned for the project, with no extra time nor costs for the client and it increases the possibilities of reaching success in terms of not only functionality but also quality.

Numerous techniques have been developed in order to prevent attacks on web servers. Anomaly detection techniques are based on models of normal user and application behavior, interpreting deviations from the established pattern as indications of malicious activity. In this work, a systematic review of the use of anomaly detection techniques in the prevention and detection of web attacks is undertaken; in particular, we used the standardized method of a systematic review of literature in the field of computer science, proposed by Kitchenham. This method is applied to a set of 88 papers extracted from a total of 8041 reviewed papers, which have been published in notable journals. This paper discusses the process carried out in this systematic review, as well as the results and findings obtained to identify the current state of the art of web anomaly detection.

The design of the techniques and algorithms used by the static, dynamic and interactive security testing tools differ. Therefore, each tool detects to a greater or lesser extent each type of vulnerability for which they are designed for. In addition, their different designs mean that they have different percentages of false positives. In order to take advantage of the possible synergies that different analysis tools types may have, this paper combines several static, dynamic and interactive analysis security testing tools—static white box security analysis (SAST), dynamic black box security analysis (DAST) and interactive white box security analysis (IAST), respectively. The aim is to investigate how to improve the effectiveness of security vulnerability detection while reducing the number of false positives. Specifically, two static, two dynamic and two interactive security analysis tools will be combined to study their behavior using a specific benchmark for OWASP Top Ten security vulnerabilities and taking into account various scenarios of different criticality in terms of the applications analyzed. Finally, this study analyzes and discuss the values of the selected metrics applied to the results for each n-tools combination.

Featured Application: The systematic and methodological process of analysis described in this document will provide a complete understanding of the life cycle of a malware specimen in terms of its behavior, operation, interaction with the environment, methods of concealment and obfuscation, system updates, and communications.Abstract: Malware threats pose new challenges to analytic and reverse engineering tasks. It is needed for a systematic approach to that analysis, in an attempt to fully uncover their underlying attack vectors and techniques and find commonalities between them. In this paper, a method of malware analysis is described, together with a report of its application to the case of Flame and Red October. The method has also been used by different analysts to analyze other malware threats like 'Stuxnet', 'Dark Comet', 'Poison Ivy', 'Locky', 'Careto', and 'Sofacy Carberp'. The method presented in this work is a systematic and methodological process of analysis, whose main objective is the acquisition of knowledge as well as to gain a full understanding of a particular malware. Using the proposed method to analyze two well-known malware as 'Flame' and 'Red October' will help to understand the added value of the method.

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