Defense Advanced Research Projects AgencyTagged Content List

Analytics for Data at Massive Scales

Extracting information from large data sets

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Training, which is conducted in classroom, field, and virtual settings, is a critical element of military readiness. Large-scale social networks, interactive content, and ubiquitous mobile access are emerging as driving technologies in education and training. At the same time, education analytics presents new opportunities for assessing the effectiveness of training strategies, understanding trends and effects in large volumes of education data, and relating these back to alternative modes of instruction.
Malicious actors in cyberspace currently operate with little fear of being caught due to the fact that it is extremely difficult, in some cases perhaps even impossible, to reliably and confidently attribute actions in cyberspace to individuals. The reason cyber attribution is difficult stems at least in part from a lack of end-to-end accountability in the current Internet infrastructure.
Dramatic success in machine learning has led to a torrent of Artificial Intelligence (AI) applications. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of these systems is limited by the machine’s current inability to explain their decisions and actions to human users (Figure 1). The Department of Defense (DoD) is facing challenges that demand more intelligent, autonomous, and symbiotic systems. Explainable AI—especially explainable machine learning—will be essential if future warfighters are to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners.
The threat of distributed denial of service (DDoS) attacks has been well-recognized in the data networking world for two decades. Such attacks are orchestrated by sets of networked hosts that collectively act to disrupt or deny access to information, communications or computing capabilities, generally by exhausting critical resources such as bandwidth, processor capacity or memory of targeted resources.
The Geospatial Cloud Analytics (GCA) program is developing technology to rapidly access the most up-to-date commercial and open-source satellite imagery, as well as automated machine learning tools to analyze this data. Current approaches to geospatial analysis are ad hoc and time intensive, as they require gathering and curating data from a large number of available sources, downloading the data to specific locations, and running it through separate suites of analytics tools.