Automated CVE Analysis: Harnessing Machine Learning In Designing Question-Answering Models For Cybersecurity Information Extraction

• URL: http://arxiv.org/abs/2412.16484v1
• Date: Sat, 21 Dec 2024 04:50:45 GMT
• Title: Automated CVE Analysis: Harnessing Machine Learning In Designing Question-Answering Models For Cybersecurity Information Extraction
• Authors: Tanjim Bin Faruk,
• Abstract summary: Question Answering (QA) systems play a pivotal role in the mapping of relationships between various data points. In the cybersecurity context, QA systems encounter unique challenges due to the need to interpret and answer questions based on a wide array of domain-specific information. This paper presents a novel dataset and describes a machine learning model trained on this dataset for the QA task.
• Score: 0.0
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• Abstract: The vast majority of cybersecurity information is unstructured text, including critical data within databases such as CVE, NVD, CWE, CAPEC, and the MITRE ATT&CK Framework. These databases are invaluable for analyzing attack patterns and understanding attacker behaviors. Creating a knowledge graph by integrating this information could unlock significant insights. However, processing this large amount of data requires advanced deep-learning techniques. A crucial step towards building such a knowledge graph is developing a robust mechanism for automating the extraction of answers to specific questions from the unstructured text. Question Answering (QA) systems play a pivotal role in this process by pinpointing and extracting precise information, facilitating the mapping of relationships between various data points. In the cybersecurity context, QA systems encounter unique challenges due to the need to interpret and answer questions based on a wide array of domain-specific information. To tackle these challenges, it is necessary to develop a cybersecurity-specific dataset and train a machine learning model on it, aimed at enhancing the understanding and retrieval of domain-specific information. This paper presents a novel dataset and describes a machine learning model trained on this dataset for the QA task. It also discusses the model's performance and key findings in a manner that maintains a balance between formality and accessibility.

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