Meaning-based machine learning for information assurance

AbstractThis 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

Lingüística computacional y esteganografía lingüística. Distribuyendo información oculta con recursos mínimos

Computational linguistics and linguistic steganography could allow to design useful systems in the protection / privacy of digital communications and digital language watermarking. However, building these systems is not always possible provided a series of conditions are not met. This article investigates whether it is possible to design procedures to hide information in natural language using minimal linguistic and computational resources. An algorithm is proposed and implemented, arguing for the usefulness and security of such proposals.La lingüística computacional puede ser aprovechada junto a la ciencia de la esteganografía lingüística para diseñar sistemas útiles en la protección/privacidad de las comunicaciones digitales y en el marcado digital de textos. No obstante, para poder llevar a cabo tal tarea se requiere de una serie de condiciones que no siempre se dan. En este artículo se investiga si es posible diseñar procedimientos que permitan ocultar información en lenguaje natural utilizando la mínima cantidad de recursos tanto lingüísticos como computacionales. Se propone un algoritmo y se implementa, razonando posteriormente a favor de la utilidad y la seguridad de propuestas de este tipo

Knowledge modeling of phishing emails

This dissertation investigates whether or not malicious phishing emails are detected better when a meaningful representation of the email bodies is available. The natural language processing theory of Ontological Semantics Technology is used for its ability to model the knowledge representation present in the email messages. Known good and phishing emails were analyzed and their meaning representations fed into machine learning binary classifiers. Unigram language models of the same emails were used as a baseline for comparing the performance of the meaningful data. The end results show how a binary classifier trained on meaningful data is better at detecting phishing emails than a unigram language model binary classifier at least using some of the selected machine learning algorithms