A hybrid transformer-based BERT and LSTM approach for vulnerability classification problems

Mounesh Marali, R. Dhanalakshmi and Narendran Rajagopalan

International Journal of Mathematics in Operational Research, 2024, vol. 28, issue 3, 275-295

Abstract: Industrial control systems (ICSs) are vulnerable to cyberattacks due to the growing corporate network and internet connectivity. Ensuring proper security and response to cyberattacks is critical as they can impact ICS performance, reliability, safety, and availability, leading to catastrophic losses. Cyber threat intelligence (CTI) helps companies avoid cyberattacks by relying on vulnerability databases such as CVEs from national vulnerability databases (NVDs). However, these databases may include flaws that compromise security solutions and hinder attack mitigation. To address this issue, automated categorisation of vulnerability data using deep learning (DL) is proposed. The study found that LSTM-tuned BERT designs outperform standard models in precision, F1 score, accuracy, and recall. Implementing DL vulnerability categorisation can enhance ICS security and decrease the threat environment.

Keywords: vulnerability; classification; LSTM; BERT; cyber threat intelligence. (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=140067 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmore:v:28:y:2024:i:3:p:275-295

Access Statistics for this article

More articles in International Journal of Mathematics in Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().