Towards a relation extraction framework for cyber-security concepts
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Abstract (arXiv)
In order to assist security analysts in obtaining information pertaining to their network, such as novel vulnerabilities, exploits, or patches, information retrieval methods tailored to the security domain are needed. As labeled text data is scarce and expensive, we follow developments in semi-supervised Natural Language Processing and implement a bootstrapping algorithm for extracting security entities and their relationships from text. The algorithm requires little input data, specifically, a few relations or patterns (heuristics for identifying relations), and incorporates an active learning component which queries the user on the most important decisions to prevent drifting from the desired relations. Preliminary testing on a small corpus shows promising results, obtaining precision of .82.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Apr 2015.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 2; 2024: 1.
9 papers.
- 2022Looking Beyond IoCs: Automatically Extracting Attack Patterns from External CTIcited by 27
- 2021What are the attackers doing now? Automating cyber threat intelligence extraction from text on pace with the changing threat landscape: A surveycited by 14
- 2022Towards Automated Classification of Attackers' TTPs by combining NLP with ML Techniquescited by 5
- 2022From Threat Reports to Continuous Threat Intelligence: A Comparison of Attack Technique Extraction Methods from Textual Artifactscited by 3
- 2022Multi-features based Semantic Augmentation Networks for Named Entity Recognition in Threat Intelligencecited by 1
- 2023Constructing a Knowledge Graph from Textual Descriptions of Software Vulnerabilities in the National Vulnerability Databasecited by 1
- 2024LSTM Recurrent Neural Networks for Cybersecurity Named Entity Recognitioncited by 1
- 2021A Deep Learning Approach for Ontology Enrichment from Unstructured Textcited by 0
- 2023Automated Mapping of CVE Vulnerability Records to MITRE CWE Weaknessescited by 0
- 2024LSTM Recurrent Neural Networks for Cybersecurity Named Entity Recognition30 Aug 2024
- 2023Constructing a Knowledge Graph from Textual Descriptions of Software Vulnerabilities in the National Vulnerability Database30 Apr 2023
- 2023Automated Mapping of CVE Vulnerability Records to MITRE CWE Weaknesses13 Apr 2023
- 2022Looking Beyond IoCs: Automatically Extracting Attack Patterns from External CTI1 Nov 2022
- 2022From Threat Reports to Continuous Threat Intelligence: A Comparison of Attack Technique Extraction Methods from Textual Artifacts5 Oct 2022
- 2022Towards Automated Classification of Attackers' TTPs by combining NLP with ML Techniques18 Jul 2022
- 2022Multi-features based Semantic Augmentation Networks for Named Entity Recognition in Threat Intelligence1 Jul 2022
- 2021A Deep Learning Approach for Ontology Enrichment from Unstructured Text16 Dec 2021
- 2021What are the attackers doing now? Automating cyber threat intelligence extraction from text on pace with the changing threat landscape: A survey14 Sep 2021