Cybersecurity-Related Tweet Classification by Explainable Deep Learning

Giacomo Iadarola, Fabio Martinelli, Francesco Mercaldo, Francesco Mercaldo, Luca Petrillo, Antonella Santone

2024

Abstract

The use of computing devices such as computers, smartphones, and IoT systems has increased exponentially over the past decade. Given this great expansion, it becomes important to identify and correct the vulnerabilities present to ensure the safety of systems and people. Over time, many official entities have emerged that publish news about these vulnerabilities; in addition to these sources, however, social media, such as X (commonly referred to by its former name Twitter), can be used to learn about these vulnerabilities even before they are made public. The goal of this work is to create clusters of tweets, which are grouped according to the description of the vulnerability in the relevant text. This process is accomplished through the use of a combination of two Doc2Vec models and a variant of a BERT model, which allow a text document to be converted into its numerical representation. Once this step was completed, K-means, an unsupervised model for performing clustering, was used, which through this numerical representation obtained in the previous step, groups tweets based on text content.

Paper Citation

in Harvard Style

Iadarola G., Martinelli F., Mercaldo F., Petrillo L. and Santone A. (2024). Cybersecurity-Related Tweet Classification by Explainable Deep Learning. In Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP; ISBN 978-989-758-683-5, SciTePress, pages 438-445. DOI: 10.5220/0012411100003648

in Bibtex Style

@conference{icissp24,
author={Giacomo Iadarola and Fabio Martinelli and Francesco Mercaldo and Luca Petrillo and Antonella Santone},
title={Cybersecurity-Related Tweet Classification by Explainable Deep Learning},
booktitle={Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP},
year={2024},
pages={438-445},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012411100003648},
isbn={978-989-758-683-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP
TI - Cybersecurity-Related Tweet Classification by Explainable Deep Learning
SN - 978-989-758-683-5
AU - Iadarola G.
AU - Martinelli F.
AU - Mercaldo F.
AU - Petrillo L.
AU - Santone A.
PY - 2024
SP - 438
EP - 445
DO - 10.5220/0012411100003648
PB - SciTePress