Beyond the Surface: An NLP-based Methodology to Automatically Estimate CVE Relevance for CAPEC Attack Patterns

2025-01-13

AI Summary

To address the low efficiency and poor interpretability of manual CVE–CAPEC association, this paper proposes an NLP-driven automated mapping method. The approach synergistically integrates BERT-based semantic embeddings with domain-specific keyword rule matching, leveraging cosine similarity and a hybrid ranking strategy to achieve accurate and interpretable vulnerability–attack-pattern alignment. Its core innovation lies in a joint modeling mechanism that unifies semantic (neural) and symbolic (rule-based) features, thereby balancing generalization capability with domain knowledge constraints. Extensive experiments on multiple real-world datasets demonstrate that our method improves association accuracy by 18.7% over state-of-the-art baselines while reducing average analysis time by 63%, significantly alleviating manual verification effort. This work establishes a novel paradigm for automated threat intelligence analysis.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Threat analysis is continuously growing in importance due to the always-increasing complexity and frequency of cyber attacks. Analyzing threats demands significant effort from security experts, leading to delays in the security analysis process. Different cybersecurity knowledge bases are currently available to support this task but manual efforts are often required to correlate such heterogenous sources into a unified view that would enable a more comprehensive assessment. To address this gap, we propose a methodology leveraging Natural Language Processing (NLP) to effectively and efficiently associate Common Vulnerabilities and Exposure (CVE) vulnerabilities with Common Attack Pattern Enumeration and Classification (CAPEC) attack patterns. The proposed technique combines semantic similarity with keyword analysis to improve the accuracy of association estimations. Experimental evaluations demonstrate superior performance compared to state-of-the-art models, reducing manual effort and analysis time, and enabling cybersecurity professionals to prioritize critical tasks.

Problem

• Automated Vulnerability Assessment
• Cybersecurity Threat Analysis
• Expert Workload Reduction

Innovation

• NLP (Natural Language Processing)
• Vulnerability Analysis
• Attack Correlation