Shifting Left for Machine Learning: An Empirical Study of Security Weaknesses in Supervised Learning-based Projects-Reference-Cited by-同舟云学术

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Shifting Left for Machine Learning: An Empirical Study of Security Weaknesses in Supervised Learning-based Projects

Published:2022-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)
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Author:

Bhuiyan Farzana Ahamed¹,Prowell Stacy²,Shahriar Hossain³,Wu Fan⁴,Rahman Akond¹

Affiliation:

1. Tennessee Tech University,Department of Computer Science,Cookeville,TN,USA

2. Oak Ridge National Laboratory,Oak Ridge,TN,USA

3. Kennesaw State University,Department of Information Technology,Marietta,GA,USA

4. Tuskegee University,Department of Computer Science,Tuskegee,AL,USA

Funder

National Science Foundation

UT-Battelle, LLC

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9842455/9842394/09842530.pdf?arnumber=9842530

Reference46 articles.

1. Hidden tech-nical debt in machine learning systems;sculley;Advances in neural information processing systems,2015

2. Automatically patching errors in deployed software

3. A Python Security Analysis Framework in Integrity Verification and Vulnerability Detection

4. Static analysis tools as early indicators of pre-release defect density;nagappan;Proceedings 27th International Conference on Software Engineering 2005 ICSE 2005,2005

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