A Threat Intelligence Event Extraction Conceptual Model for Cyber Threat Intelligence Feeds

TL;DR

This paper reviews current CTI data collection techniques, emphasizing AI's role, and introduces a new conceptual model integrating advanced NLP methods to improve threat event extraction and cybersecurity response.

Contribution

It provides a systematic review of CTI data collection methods and proposes the XBC conceptual model combining XLM-RoBERTa, BiGRU, and CRF for enhanced threat intelligence processing.

Findings

AI-driven methods improve threat detection accuracy

The XBC model addresses existing research gaps

Enhanced preprocessing efficiency for multilingual threat data

Abstract

In response to the escalating cyber threats, the efficiency of Cyber Threat Intelligence (CTI) data collection has become paramount in ensuring robust cybersecurity. However, existing works encounter significant challenges in preprocessing large volumes of multilingual threat data, leading to inefficiencies in real-time threat analysis. This paper presents a systematic review of current techniques aimed at enhancing CTI data collection efficiency. Additionally, it proposes a conceptual model to further advance the effectiveness of threat intelligence feeds. Following the PRISMA guidelines, the review examines relevant studies from the Scopus database, highlighting the critical role of artificial intelligence (AI) and machine learning models in optimizing CTI data preprocessing. The findings underscore the importance of AI-driven methods, particularly supervised and unsupervised learning, in significantly improving the accuracy of threat detection and event extraction, thereby strengthening cybersecurity. Furthermore, the study identifies a gap in the existing research and introduces XBC conceptual model integrating XLM-RoBERTa, BiGRU, and CRF, specifically developed to address this gap. This paper contributes conceptually to the field by providing a detailed analysis of current CTI data collection techniques and introducing an innovative conceptual model to enhance future threat intelligence capabilities.