EAGER: Human-Centric Predictive Analytics of Cyber-Threats: a Temporal Dynamics Approach

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Cybersecurity is paramount to protecting national interests in domains that include but extend well beyond defense and finance. However, current state-of-the-art cyber-defenses have severely limited predictive and attribution capabilities. Detecting and understanding cyber attacks is not sufficient--we can liken that to "studying symptoms instead of a disease." This research carries out a synergistic approach to integrating cyber data forensics with human-centric social network analysis under a common framework. The three major activities of this project are as follows: (a) comprehensive models of cyber-attack characteristics are developed using feature extraction techniques on diverse data sources, (b) adversarial groups are classified according to their feature similarities, and (c) group classification is enhanced using analytic techniques from social network science. To accomplish these activities: (1) different models for constructing joint representations of computer and social networks are investigated within a multi-mode graph framework; (2) data reduction and feature extraction techniques are developed for associating large datasets with this unified graph model; (3) an existing system is leveraged to discover invisible and missing links between adversarial networks of individuals; and (4) social network models and tools, as well as case studies, are applied to infer adversarial group typology. This research is expected to benefit computer science, cyber security, and social sciences by improving detection methods for cyber attacks. Applying time-series analysis in creative ways to multi-mode networks should contribute to the field of artificial intelligence. This joint work should apply also in fields such as health care, marketing, or forecasting technology adoption trends.