A hybrid transformer-based BERT and LSTM approach for vulnerability classification problems Online publication date: Fri, 19-Jul-2024
by Mounesh Marali; R. Dhanalakshmi; Narendran Rajagopalan
International Journal of Mathematics in Operational Research (IJMOR), Vol. 28, No. 3, 2024
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Mathematics in Operational Research (IJMOR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook