Comparing Support Vector Machine and Neural Network Classifiers of CVE Vulnerabilities

Grzegorz Blinowski, Paweł Piotrowski, Michał Wiśniewski

2021

Abstract

The Common Vulnerabilities and Exposures (CVE) database is the largest publicly available source of structured data on software and hardware vulnerability. In this work, we analyze the CVE database in the context of IoT device and system vulnerabilities. We employ and compare support vector machine (SVM) and neural network (NN) algorithms on a selected subset of the CVE database to classify vulnerability records in this framework. Our scope of interest consists of records that describe vulnerabilities of potential IoT devices of different types, such as home appliances, SCADA (industry) devices, mobile controllers, networking equipment and others. The purpose of this work is to develop and test an automated system of recognition of IoT vulnerabilities to test two different methods of classification (SVM and NN) and to find an optimal timeframe for training (historical) data.

Paper Citation

in Harvard Style

Blinowski G., Piotrowski P. and Wiśniewski M. (2021). Comparing Support Vector Machine and Neural Network Classifiers of CVE Vulnerabilities. In Proceedings of the 18th International Conference on Security and Cryptography - Volume 1: SECRYPT, ISBN 978-989-758-524-1, pages 734-740. DOI: 10.5220/0010574807340740

in Bibtex Style

@conference{secrypt21,
author={Grzegorz Blinowski and Paweł Piotrowski and Michał Wiśniewski},
title={Comparing Support Vector Machine and Neural Network Classifiers of CVE Vulnerabilities},
booktitle={Proceedings of the 18th International Conference on Security and Cryptography - Volume 1: SECRYPT,},
year={2021},
pages={734-740},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010574807340740},
isbn={978-989-758-524-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Security and Cryptography - Volume 1: SECRYPT,
TI - Comparing Support Vector Machine and Neural Network Classifiers of CVE Vulnerabilities
SN - 978-989-758-524-1
AU - Blinowski G.
AU - Piotrowski P.
AU - Wiśniewski M.
PY - 2021
SP - 734
EP - 740
DO - 10.5220/0010574807340740