Thought Diversity

Moving Big-Data Analysis from a ‘Forensic Sport’ to a ‘Contact Sport’ Using Machine Learning and Thought Diversity

ABSTRACT

Data characterization, trending, correlation, and sense making are almost always performed after the data is collected. As a result, big-data analysis is an inherently forensic (after-the-fact) process. In order for network defenders to be more effective in the big-data collection, analysis, and intelligence reporting mission space, first-order analysis (initial characterization and correlation) must be a contact sport—that is, must happen at the point and time of contact with the data—on the sensor. This paper will use actionable examples: (1) to advocate for running Machine-Learning (ML) algorithms on the sensor as it will result in more timely, more accurate (fewer false positives), automated, scalable, and usable analyses; (2) discuss why establishing thought-diverse (variety of opinions, perspectives, and positions) analytic teams to perform and produce analysis will not only result in more effective collection, analysis, and sense making, but also increase network defenders’ ability to counter and/or neuter adversaries’ ability to deny, degrade, and destabilize U.S. networks.

Published In

Volume 14, Issue 2

Journal of Information Warfare

The definitive publication for the best and latest research and analysis on information warfare, information operations, and cyber crime. Available in traditional hard copy or online.

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The definitive publication for the best and latest research and analysis on information warfare, information operations, and cyber crime. Available in traditional hard copy or online.

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