Proceedings of the 2021 6th International Conference on Social Sciences and Economic Development (ICSSED 2021)

Application of Time Series and Clustering in Research of Information Vulnerabilities

Authors
Jing Nan, Ruiying Jin
Corresponding Author
Jing Nan
Available Online 8 April 2021.
DOI
https://doi.org/10.2991/assehr.k.210407.101How to use a DOI?
Keywords
information security, clustering, text analysis, time series analysis
Abstract
With the advent of the era of big data, computer networks have penetrated into every aspect of people’s life and work. While people enjoy the convenience brought by the Internet, they also face the threat of network security vulnerabilities, and it is urgent to strengthen network security. China National Information Security Vulnerability Database classifies information security vulnerabilities into 26 categories, such as configuration errors and SQL injection. With the increasing complexity of information security in the past two years, the traditional vulnerability classification standard has been subjected to new tests. In this paper, we use crawler technology to obtain vulnerability data from China National Information Security Vulnerability Database from 2004 to September 2020, time series analysis to predict the number of vulnerabilities, and use text analysis and clustering technology to re-cluster the vulnerabilities and derive new vulnerability classification levels and criteria.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Jing Nan
AU  - Ruiying Jin
PY  - 2021
DA  - 2021/04/08
TI  - Application of Time Series and Clustering in Research of Information Vulnerabilities
BT  - Proceedings of the 2021 6th International Conference on Social Sciences and Economic Development (ICSSED 2021)
PB  - Atlantis Press
SP  - 519
EP  - 522
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.210407.101
DO  - https://doi.org/10.2991/assehr.k.210407.101
ID  - Nan2021
ER  -