Global networking, growing computer infrastructure complexity and the ongoing migration of many private and business aspects to the electronic domain commonly mandate using cutting-edge technologies based on data analysis, machine learning, and artificial intelligence to ensure high levels of network and information system security. Transparency is a major barrier to the deployment of black box intelligent systems in high-risk domains, such as the cybersecurity domain, with the problem getting worse as machine learning models increase in complexity. In this research, explainable machine learning is used to extract information from the CIC-IDS2017 dataset and to critically contrast the knowledge attained by analyzing if–then decision tree rules with the knowledge attained by the SHAP approach. The paper compares the challenges of the knowledge extraction using the SHAP method and the if–then decision tree rules, providing guidelines regarding different approaches suited to specific situations.

Artificial intelligence has become mainstream and its applications will only proliferate. Specific measures must be done to integrate such systems into society for the general benefit. One of the tools for improving that is explainability which boosts trust and understanding of decisions between humans and machines. This research offers an update on the current state of explainable AI (XAI). Recent XAI surveys in supervised learning show convergence of main conceptual ideas. We list the applications of XAI in the real world with concrete impact. The list is short and we call to action - to validate all the hard work done in the field with applications that go beyond experiments on datasets, but drive decisions and changes. We identify new frontiers of research, explainability of reinforcement learning and graph neural networks. For the latter, we give a detailed overview of the field.

This paper deals with the mediator–wrapper architecture and observes it in more modern aspects by relating it to architectural quanta. It is an important architectural pattern that enables a more flexible and modular architecture in opposition to monolithic architectures for data source integration systems. This paper identifies certain realistic and concrete scenarios where the mediator–wrapper architecture underperforms. These issues are addressed with the extension of the architecture via the mask component type. The mask component is detailed so it can be reasoned about without prescribing a concrete programming language or paradigm but still providing a working principle. The benefits of the new mask–mediator–wrapper architecture are analytically proven in relevant scenarios. The proof includes a software shift–cost analysis whose results quantitatively show the improvement of the mask–mediator–wrapper architecture over other mediator–wrapper architecture settings. One of the applications of the new architecture is envisioned for modern data sources integration systems backing big data processing.