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Proceedings Paper

A developmental approach to learning causal models for cyber security
Author(s): Jonathan Mugan
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Paper Abstract

To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.

Paper Details

Date Published: 28 May 2013
PDF: 10 pages
Proc. SPIE 8751, Machine Intelligence and Bio-inspired Computation: Theory and Applications VII, 87510A (28 May 2013); doi: 10.1117/12.2014418
Show Author Affiliations
Jonathan Mugan, 21CT, Inc. (United States)


Published in SPIE Proceedings Vol. 8751:
Machine Intelligence and Bio-inspired Computation: Theory and Applications VII
Misty Blowers; Olga Mendoza-Schrock, Editor(s)

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