Meaning-based machine learning for information assurance

•Describes the combination of semantic knowledge bases with machine learning.•Natural language processing application for phishing detection.•Semantic machine learning improves on existing approaches.

This paper presents meaning-based machine learning, the use of semantically meaningful input data into machine learning systems in order to produce output that is meaningful to a human user where the semantic input comes from the Ontological Semantics Technology theory of natural language processing. How to bridge from knowledge-based natural language processing architectures to traditional machine learning systems is described to include high-level descriptions of the steps taken. These meaning-based machine learning systems are then applied to problems in information assurance and security that remain unsolved and feature large amounts of natural language text.