Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.

Cloud computing services consumers do not have enough reliable information about critical characteristics of their providers, such as performance, security, trust and privacy, compliance with laws and regulations, among others. Our proposal addresses these problems presenting a trust assessment framework that integrates three domains: Governance, Transparency, and Security Information. Our approach is consumer-centric and deals with trust aspects from the end-user's perspective. We use Indicators to communicate the outcomes, which aim to represent the expression of cybersecurity, manageability, and transparency of services under assessment. This paper includes an implementation proposal, prototype, and proof of concept, in which the framework was applied in a real scenario and executed over a long-term (18 months) usage simulation to verify its applicability, sensitivity, and robustness. Our study is intended for use by consumers of cloud computing who seek to know and measure levels of cybersecurity, protection of privacy, transparency of security, and high levels of quality in their services and infrastructure.