Quantifying the Significance of Cybersecurity Text through Semantic Similarity and Named Entity Recognition

Otgonpurev Mendsaikhan, Hirokazu Hasegawa, Yamaguchi Yukiko, Hajime Shimada

2020

Abstract

In order to proactively mitigate the risks of cybersecurity, security analysts have to continuously monitor threat information sources. However, the sheer amount of textual information that needs to be processed is overwhelming and requires a great deal of mundane labor. We propose a novel approach to automate this process by analyzing the text document using semantic similarity and Named Entity Recognition (NER) methods. The semantic representation of the given text has been compared with pre-defined “significant” text and, by using a NER model, the assets relevant to the organization are identified. The analysis results then act as features of the linear classifier to generate the significance score. The experimental result shows that the overall system could determine the significance of the text with 78% accuracy.

Paper Citation

in Harvard Style

Mendsaikhan O., Hasegawa H., Yukiko Y. and Shimada H. (2020). Quantifying the Significance of Cybersecurity Text through Semantic Similarity and Named Entity Recognition. In Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-399-5, pages 325-332. DOI: 10.5220/0008913003250332

in Bibtex Style

@conference{icissp20,
author={Otgonpurev Mendsaikhan and Hirokazu Hasegawa and Yamaguchi Yukiko and Hajime Shimada},
title={Quantifying the Significance of Cybersecurity Text through Semantic Similarity and Named Entity Recognition},
booktitle={Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2020},
pages={325-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008913003250332},
isbn={978-989-758-399-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Quantifying the Significance of Cybersecurity Text through Semantic Similarity and Named Entity Recognition
SN - 978-989-758-399-5
AU - Mendsaikhan O.
AU - Hasegawa H.
AU - Yukiko Y.
AU - Shimada H.
PY - 2020
SP - 325
EP - 332
DO - 10.5220/0008913003250332