arXiv AI July 15, 2026 By Bipin Chhetri, Deepika Giri, Avishek Kadel, Rabin Kumar Karki, Akbar Siami Namin

Mitigating The Effect of Class Imbalance in Data with Hierarchical and Dependable Structure

arXiv:2607. 11994v1 Announce Type: cross Abstract: Classifying cybersecurity vulnerabilities using the Common Weakness Enumeration (CWE) taxonomy is challenging due to extreme class imbalance and strong hierarchical dependencies among weakness categories.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 9

Multi-Class vs. Multi-Label BERT for CVE-to-CWE Mapping: How Taxonomy Structure Shapes the Errors

arXiv:2607. 07573v1 Announce Type: new Abstract: Assigning Common Weakness Enumeration (CWE) categories to Common Vulnerabilities and Exposures (CVE) records remains an important but largely manual step in vulnerability analysis.

By Ana Schwengber Kelm, Christian Bockermann, J\"org Frochte

llms nlp

Hugging Face Trending Papers

Jul 8

Multi-Class vs. Multi-Label BERT for CVE-to-CWE Mapping: How Taxonomy Structure Shapes the Errors

Assigning Common Weakness Enumeration (CWE) categories to Common Vulnerabilities and Exposures (CVE) records remains an important but largely manual step in vulnerability analysis. We study this task as a text classification problem and compare two modelling choices: a \emph{multi-class} formulation that predicts a single CWE per CVE and a \emph{multi-label} formulation that allows multiple assignments.

llms nlp

arXiv Machine Learning

Jul 3

From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection

arXiv:2512. 10485v2 Announce Type: replace-cross Abstract: Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiveness remains underexplored.

By Chaomeng Lu, Bert Lagaisse

llms benchmarks

arXiv Machine Learning

Jul 9

MARGIN: Margin-Aware Regularized Geometry for Imbalanced Vulnerability Detection

arXiv:2605. 10240v3 Announce Type: replace-cross Abstract: Software vulnerability detection is critical for ensuring software security and reliability.

By Yuteng Zhang, Huifang Ma, Jiahui Wei, Qingqing Li, Yafei Yang

rag safety

arXiv Machine Learning

Jun 30

ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data

arXiv:2408. 16028v4 Announce Type: replace-cross Abstract: Supervised-learning-based vulnerability detectors often fall short due to limited labelled training data.

By Weizhou Wang, Eric Liu, Xiangyu Guo, Xiao Hu, Ilya Grishchenko, David Lie

llms benchmarks

arXiv AI

Jun 9

Large Language Models for Imbalanced Classification: Diversity makes the difference

arXiv:2510. 09783v2 Announce Type: replace-cross Abstract: Oversampling is one of the most widely used approaches for addressing imbalanced classification.

By Dang Nguyen, Sunil Gupta, Kien Do, Thin Nguyen, Taylor Braund, Alexis Whitton, Svetha Venkatesh

llms fine-tuning benchmarks

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Multi-Class vs. Multi-Label BERT for CVE-to-CWE Mapping: How Taxonomy Structure Shapes the Errors

Multi-Class vs. Multi-Label BERT for CVE-to-CWE Mapping: How Taxonomy Structure Shapes the Errors

From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection

MARGIN: Margin-Aware Regularized Geometry for Imbalanced Vulnerability Detection

ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data

Large Language Models for Imbalanced Classification: Diversity makes the difference