Future Internet

This paper develops a framework for monitoring and forecasting episodes of systemic financial stress using a combination of market information, macro-financial indicators, and measures derived from time-varying correlation networks, embedded in a sequential machine-learning setting. The contribution is not tied to a single modelling innovation, but rather to the way these ingredients are brought together under an evaluation protocol designed to mimic real-time supervisory use, and to an interpretability layer that makes the resulting predictions easier to inspect. Monthly data covering the period from 2006 to 2025 are used to construct evolving correlation structures and summary indicators of market co-movement. These features are combined with standard predictors and fed into logistic regression, random forest, and gradient boosting models, all estimated in expanding windows and assessed strictly on future observations. Predictive accuracy remains limited, which is consistent with the difficulty of anticipating stress regimes several months ahead at monthly frequency, although gradient boosting attains the highest average AUC across evaluation folds and displays noticeable variation over time. Inspection of SHAP values points to instability in correlation networks, volatility conditions, and short-horizon return behaviour as recurring drivers of the predicted stress probabilities, suggesting that the models draw on information that goes beyond individual market series. Taken together, the results indicate that recurrent statistical regularities and changes in market structure can be exploited for monitoring purposes when models are trained and tested in a sequential fashion. The overall design is intended to be usable in practice and to support supervisory analysis, while remaining transparent enough to allow scrutiny of the signals driving the forecasts.

The rapid digital transformation of modern society is increasingly driven by the convergence of blockchain technologies and the Internet of Things (IoT), both of which have become foundational pillars of contemporary distributed systems [...]

Moving Target Defense (MTD) is a paradigm that has the potential to revolutionize the approach to network security. Although a significant number of papers have been published on the topic, there are still no standards or any dominant implementations of this concept. This article identifies and attempts to bridge the gap in understanding various aspects of MTD security while also defining the research directions necessary to implement MTD techniques in real-world scenarios. It discusses the security of key design principles of MTD, considers problems regarding applying MTD to real networks, and finally addresses threat modeling in the context of MTD. By aggregating various security aspects related to MTD, some of which have not been typically discussed in the available literature on the subject, this work aims to assist in designing future MTD schemes, help navigate around various security caveats, and highlight possible research directions that have not been sufficiently explored in the existing literature.