Cyberattacks are increasing every day, demanding that security incident response teams proactively determine potential threats early. Although social networks such as Twitter are a rich and up-to-date source of information where users use to tweet about different topics, it is complex to efficiently and effectively obtain results that support decision-making on a specific subject, such as cyberattacks. Therefore, in this work, we propose to use an offline mining process based on the clique percolation method over a corpus of tweets in order to generate an indexed knowledge base about cyberattacks. Results are promising to observe threats under evolution. Then, to show results properly, we generate an observatory prototype to allow cybersecurity researchers to explore threats over time and space.

Hesitant fuzzy sets have been proposed as an extension of fuzzy sets to address situations in which decision makers exhibit variations in their alternatives' assessment values. However, in real-world problems, the decision-making process has to be accomplished under situations where these assessment values may also drastically change over time. In this paper, we propose a prioritized aggregation operator to combine a time sequence of hesitant fuzzy information, where the time-based hesitancy due to changing environment is mitigated. The proposed method is applied to the service selection problem in service-based systems, where software architects must select as a group the service that has the best combination of features based on their historical assessments. We claim that the time-based hesitant fuzzy information aggregation method addresses the hesitancy at intra- and interexpert levels obtaining more robust decisions.

Service-based systems (SBS) must be able to adapt their architectural configurations during runtime in order to keep satisfied their specification models. These models are the result of design time derivation of requirements into precise and verifiable specifications by using the knowledge about the current service offerings. Unfortunately, the design time knowledge may be no longer valid during runtime. Then, nonfunctional constraints may have different numerical meanings at different time even for the same observers. Thus, specification models become obsolete affecting the SBS' capability of detecting requirement violations during runtime and therefore they trigger reconfigurations when appropriated. In order to mitigate the obsolescence of specification models, we propose to specify and verify them using the computing with words (CWW) methodology. First, non-functional properties (NFPs) of functionally-equivalent services are modeled as linguistic variables, whose domains are concepts or linguistic values instead of precise numbers. Second, architects specify at design time their requirements as linguistic decision models (LDMs) using these concepts. Third, during runtime, the CWW engine monitors the requirements satisfaction by the current chosen architectural configuration. And fourth, each time a global concept drift is detected in the NFPs of the services market, the numerical meanings are updated. Our initial results are encouraging, where our approach mitigates effectively and efficiently the obsolescence of the specification models used by SBS to drive their reconfigurations.

A common problem that mashup developers face is the discovery of APIs that suit their needs. This primary task becomes harder, tedious and time-consuming with the proliferation of new APIs. As humans, we learn by example, following community previous decisions when creating mashups. Most techniques do not consider at all reusing this social information. In this paper, we propose to combine current discovery techniques (exploration) with social information (exploitation). Our preliminary results show that by considering the reciprocal influence of both sources, the discovery process reveals APIs that would remain with low rank because the preferential attachment (popularity) and/or the lack of better descriptions (discovery techniques). We present a case study focusing on a public Web-based API registry.

The autopietic/allopietic duality has been proposed to address the fact that Software systems must look to their own survival besides their successful mission completion. To the best of our knowledge, there are no explicit computational models to operationalize and test this distinction. This article describes a framework to add autopietic capabilities to composite Software systems, by using an external self-organized market full of service providers willing to provide services for satisfying new requirements and recovering actions. The approach is exemplified when a QoS agreement violation occurs in a composite application system.