ThreatZoom: CVE2CWE using Hierarchical Neural Network Article Swipe
Ehsan Aghaei
,
Waseem Shadid
,
Ehab Al‐Shaer
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2009.11501
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2009.11501
The Common Vulnerabilities and Exposures (CVE) represent standard means for sharing publicly known information security vulnerabilities. One or more CVEs are grouped into the Common Weakness Enumeration (CWE) classes for the purpose of understanding the software or configuration flaws and potential impacts enabled by these vulnerabilities and identifying means to detect or prevent exploitation. As the CVE-to-CWE classification is mostly performed manually by domain experts, thousands of critical and new CVEs remain unclassified, yet they are unpatchable. This significantly limits the utility of CVEs and slows down proactive threat mitigation. This paper presents the first automatic tool to classify CVEs to CWEs. ThreatZoom uses a novel learning algorithm that employs an adaptive hierarchical neural network which adjusts its weights based on text analytic scores and classification errors. It automatically estimates the CWE classes corresponding to a CVE instance using both statistical and semantic features extracted from the description of a CVE. This tool is rigorously tested by various datasets provided by MITRE and the National Vulnerability Database (NVD). The accuracy of classifying CVE instances to their correct CWE classes are 92% (fine-grain) and 94% (coarse-grain) for NVD dataset, and 75% (fine-grain) and 90% (coarse-grain) for MITRE dataset, despite the small corpus.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2009.11501
- https://arxiv.org/pdf/2009.11501
- OA Status
- green
- Cited By
- 8
- References
- 10
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3089012561
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3089012561Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2009.11501Digital Object Identifier
- Title
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ThreatZoom: CVE2CWE using Hierarchical Neural NetworkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-09-24Full publication date if available
- Authors
-
Ehsan Aghaei, Waseem Shadid, Ehab Al‐ShaerList of authors in order
- Landing page
-
https://arxiv.org/abs/2009.11501Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2009.11501Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2009.11501Direct OA link when available
- Concepts
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Computer science, Artificial neural network, Vulnerability (computing), Artificial intelligence, Machine learning, Domain (mathematical analysis), Deep neural networks, Data mining, Computer security, Mathematical analysis, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2022: 4, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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10Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.analytic | 122 |
| abstract_inverted_index.classify | 98 |
| abstract_inverted_index.critical | 67 |
| abstract_inverted_index.dataset, | 187, 196 |
| abstract_inverted_index.datasets | 158 |
| abstract_inverted_index.experts, | 64 |
| abstract_inverted_index.features | 143 |
| abstract_inverted_index.instance | 137 |
| abstract_inverted_index.learning | 106 |
| abstract_inverted_index.manually | 61 |
| abstract_inverted_index.presents | 92 |
| abstract_inverted_index.provided | 159 |
| abstract_inverted_index.publicly | 11 |
| abstract_inverted_index.security | 14 |
| abstract_inverted_index.semantic | 142 |
| abstract_inverted_index.software | 35 |
| abstract_inverted_index.standard | 7 |
| abstract_inverted_index.Exposures | 4 |
| abstract_inverted_index.algorithm | 107 |
| abstract_inverted_index.automatic | 95 |
| abstract_inverted_index.estimates | 129 |
| abstract_inverted_index.extracted | 144 |
| abstract_inverted_index.instances | 173 |
| abstract_inverted_index.performed | 60 |
| abstract_inverted_index.potential | 40 |
| abstract_inverted_index.proactive | 87 |
| abstract_inverted_index.represent | 6 |
| abstract_inverted_index.thousands | 65 |
| abstract_inverted_index.CVE-to-CWE | 56 |
| abstract_inverted_index.ThreatZoom | 102 |
| abstract_inverted_index.rigorously | 154 |
| abstract_inverted_index.Enumeration | 26 |
| abstract_inverted_index.classifying | 171 |
| abstract_inverted_index.description | 147 |
| abstract_inverted_index.identifying | 47 |
| abstract_inverted_index.information | 13 |
| abstract_inverted_index.mitigation. | 89 |
| abstract_inverted_index.statistical | 140 |
| abstract_inverted_index.(fine-grain) | 181, 190 |
| abstract_inverted_index.hierarchical | 112 |
| abstract_inverted_index.unpatchable. | 76 |
| abstract_inverted_index.Vulnerability | 165 |
| abstract_inverted_index.automatically | 128 |
| abstract_inverted_index.configuration | 37 |
| abstract_inverted_index.corresponding | 133 |
| abstract_inverted_index.exploitation. | 53 |
| abstract_inverted_index.significantly | 78 |
| abstract_inverted_index.unclassified, | 72 |
| abstract_inverted_index.understanding | 33 |
| abstract_inverted_index.(coarse-grain) | 184, 193 |
| abstract_inverted_index.classification | 57, 125 |
| abstract_inverted_index.Vulnerabilities | 2 |
| abstract_inverted_index.vulnerabilities | 45 |
| abstract_inverted_index.vulnerabilities. | 15 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 93 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.87997794 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |