Concerns of cyber-security threats are increasingly becoming a part of everyday operations of cyber-physical systems, especially in the context of critical infrastructures. However, despite the tight integration of cyber and physical components in modern critical infrastructures, the monitoring of cyber and physical subsystems is still done separately. For successful health monitoring of such systems, a holistic approach is needed. In this paper, we present an approach for holistic health monitoring of cyber-physical systems based on cyber and physical anomaly detection and correlation. We provide a data-driven approach for the detection of cyber and physical anomalies based on machine learning. The benefits of the presented approach are: 1) integrated architecture that supports acquisition and real-time analysis of both cyber and physical data; 2) a metric for holistic health monitoring that allows for differentiation between physical faults, cyber intrusion, and cyber-physical attacks. We present experimental analysis on a power-grid use case using the IEEE-33 bus model. The system was tested on several types of attacks such as network scan, Denial of Service (DOS), and malicious command injections.

Detection and identification of misinformation and fake news is a complex problem that intersects several disciplines, ranging from sociology to computer science and mathematics. In this work, we focus on social media analyzing characteristics that are independent of the text language (language-independent) and social context (location-independent) and common to most social media, not only Twitter as mostly analyzed in the literature. Specifically, we analyze temporal and structural characteristics of information flow in the social networks and we evaluate the importance and effect of two different types of features in the detection process of fake rumors. Specifically, we extract epidemiological features exploiting epidemiological models for spreading false rumors; furthermore, we extract graph-based features from the graph structure of the information cascade of the social graph. Using these features, we evaluate them for fake rumor detection with 3 configurations: (i) using only epidemiological features, (ii) using only graph-based features, and (iii) using the combination of epidemiological and graph-based features. Evaluation is performed with a Gradient Boosting classifier on two benchmark fake rumor detection datasets. Our results demonstrate that epidemiological models fit rumor propagation well, while graph-based features lead to more effective classification of rumors; the combination of epidemiological and graph-based features leads to improved performance.