Abstract: A system for automated cyber physical threat campaign analysis and attribution, comprising a multi-dimensional time series and graph hybrid data server, an automated planning service module, and a directed computation graph module. A dataset is gathered from a monitored network and aggregated into a cyber-physical systems graph. Cyberattack simulations on the monitored network are made using exogenously collected data as input. Metrics are generated based on the cyber-physical systems graph and results from the cyberattack simulations, and the generated metrics are used to develop a threat profile.

Abstract: A system for mitigation of cyberattacks employing an advanced cyber decision platform comprising a time series data retrieval module, a directed computational graph module, an outcome simulation module, and an observation module. The time series data retrieval module monitors cybersecurity related data from multiple sources, and continuously monitors traffic on a client network. The directed computational graph module analyzes the retrieved data for baseline pattern determination, and analyzes the data for anomalous occurrences. The outcome simulation module performs predictive simulation transformations on data provided by other modules of the platform and provides results as needed. The observation module formats data to maximize impact of included information and data.

Abstract: A distributed system for large volume deep web data extraction that is extremely scalable, allows multiple heterogeneous concurrent searches, has power web scrape result processing capabilities and uses a well defined, highly customizable, simplified, search agent configuration interface requiring minimal specialized programming knowledge. A scrape campaign control module receives scrape control and web spider configuration parameters through either a command line interface of an HTTP based application programming interface. The control module uses those parameters to have an arbitrary plurality of web spiders created and deployed by a plurality of servers. Scrape campaign results are presented as prescribed.

Abstract: A system and method for the capture, storage and analysis of multidimensional time series data from sources with heterogeneous reporting profiles has been devised. Data from sets of sensors that send variable amounts of multidimensional data at irregular intervals is received by a data processing device which processes the raw data to extract the parameters of interest and held until a preset number of sensor events or amount of stream data is received. The data is then committed to a data store until time of analysis. Older data is written to archive storage. The system allows complex selection and transformation of data store data by robust query language.