An Accuracy-Maximization Approach for Claims Classifiers in Document Content Analytics for Cybersecurity

An Accuracy-Maximization Approach for Claims Classifiers in Document Content Analytics for Cybersecurity

This paper presents our research approach and findings towards maximizing the accuracy of our classifier of feature claims for cybersecurity literature analytics, and introduces the resulting model ClaimsBERT. Its architecture, after extensive evaluations of different approaches, introduces a featur...

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Bibliographic Details
Main Authors: Kimia Ameri, Michael Hempel, Hamid Sharif, Juan Lopez Jr., Kalyan Perumalla
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Journal of Cybersecurity and Privacy
Subjects:
natural language processing
BERT
transfer learning
convolution neural network
classification
cybersecurity
Online Access:https://www.mdpi.com/2624-800X/2/2/22

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