Noisy label learning for security defects

Author:

Croft Roland1,Babar M. Ali1,Chen Huaming1

Affiliation:

1. The University of Adelaide, Adelaide, Australia

Funder

Cyber Security Cooperative Research Centre

Publisher

ACM

Reference76 articles.

1. Snoring: A Noise in Defect Prediction Datasets

2. Challenges with Label Quality for Supervised Learning

3. Afsah Anwar , Ahmed Abusnaina , Songqing Chen , Frank Li , and David Mohaisen . 2021. Cleaning the NVD: Comprehensive quality assessment, improvements, and analyses . IEEE Transactions on Dependable and Secure Computing ( 2021 ). Afsah Anwar, Ahmed Abusnaina, Songqing Chen, Frank Li, and David Mohaisen. 2021. Cleaning the NVD: Comprehensive quality assessment, improvements, and analyses. IEEE Transactions on Dependable and Secure Computing (2021).

4. Learning from positive and unlabeled data: a survey

5. Class noise detection based on software metrics and ROC curves

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2. When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection;2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE);2023-09-11

3. Data Quality for Software Vulnerability Datasets;2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE);2023-05

4. A Study on Vulnerability Code Labeling Method in Open-Source C Programs;Lecture Notes in Computer Science;2023

5. Learning About the Adversary;Advances in Information Security;2023

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