#5383. A Deep Learning Approach for Classifying Vulnerability Descriptions Using Self Attention Based Neural Network

August 2026publication date
Proposal available till 13-05-2025
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Journal’s subject area:
Strategy and Management;
Information Systems;
Computer Networks and Communications;
Hardware and Architecture;
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Cyber threat intelligence (CTI) refers to essential knowledge used by organizations to prevent or mitigate against cyber attacks. Vulnerability databases such as CVE and NVD are crucial to cyber threat intelligence, but also provide information leveraged in hundreds of security products worldwide. However, previous studies have shown that these vulnerability databases sometimes contain errors and inconsistencies which have to be manually checked by security professionals. Such inconsistencies could threaten the integrity of security products and hamper attack mitigation efforts. Hence, to assist the security community with more accurate and time-saving validation of vulnerability data, we propose an automated vulnerability classification system based on deep learning. Our proposed system utilizes a self-attention deep neural network (SA-DNN) model and text mining approach to identify the vulnerability category from the description text contained within a report.
Keywords:
Common vulnerabilities and exposures; Cyber threat intelligence; Deep learning; Graph convolutional neural network; Self attention neural network; Text mining; Vulnerability classification

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