International Journal of Network Security & Its Applications (IJNSA) - ERA, WJCI Indexed

International Journal of Network Security & Its Applications (IJNSA) - ERA, WJCI Indexed

ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)

https://airccse.org/journal/ijnsa.html

A Robust Cybersecurity Topic Classification Tool

Elijah Pelofske1, Lorie M. Liebrock1 and Vincent Urias2, 1New Mexico Institute of Mining and Technology, USA, 2Sandia National Laboratories, New Mexico, USA

Abstract

In this research, we use user defined labels from three internet text sources (Reddit, StackExchange, Arxiv) to train 21 different machine learning models for the topic classification task of detecting cybersecurity discussions in natural English text. We analyze the false positive and false negative rates of each of the 21 model’s in cross validation experiments. Then we present a Cybersecurity Topic Classification (CTC) tool, which takes the majority vote of the 21 trained machine learning models as the decision mechanism for detecting cybersecurity related text. We also show that the majority vote mechanism of the CTC tool provides lower false negative and false positive rates on average than any of the 21 individual models. We show that the CTC tool is scalable to the hundreds of thousands of documents with a wall clock time on the order of hours.

Keywords

cybersecurity, topic modeling, text classification, machine learning, neural networks, natural language processing, StackExchange, Reddit, Arxiv, social media

Abstract URL: http://aircconline.com/abstract/ijnsa/v14n1/14122ijnsa01.html

Original Source URL: https://aircconline.com/ijnsa/V14N1/14122ijnsa01.pdf

Volume URL: https://airccse.org/journal/jnsa22_current.html