Detecting Backdoors in Collaboration Graphs of Software Repositories-Reference-Cited by-同舟云学术

Detecting Backdoors in Collaboration Graphs of Software Repositories

Published:2023-04-24 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy
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Author:

Ganz Tom¹^ORCID,Ashraf Inaam²^ORCID,Härterich Martin¹^ORCID,Rieck Konrad³^ORCID

Affiliation:

1. SAP SE, Karlsruhe, Germany

2. Bielefeld University, Bielefeld, Germany

3. Technische Universität Berlin, Berlin, Germany

Funder

German Federal Ministry of Education and Research (BMBF)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3577923.3583657

Reference45 articles.

1. Daniel Arp , Erwin Quiring , Feargus Pendlebury , Alexander Warnecke , Fabio Pierazzi , Christian Wressnegger , Lorenzo Cavallaro , and Konrad Rieck . 2022 . Dos and Don'ts of Machine Learning in Computer Security . In 31st USENIX Security Symposium (USENIX Security 22) . USENIX Association, Boston, MA. https://www.usenix.org/conference/usenixsecurity22/presentation/arp Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, and Konrad Rieck. 2022. Dos and Don'ts of Machine Learning in Computer Security. In 31st USENIX Security Symposium (USENIX Security 22). USENIX Association, Boston, MA. https://www.usenix.org/conference/usenixsecurity22/presentation/arp

2. Saikat Chakraborty Rahul Krishna Yangruibo Ding and Baishakhi Ray. 2020. Deep Learning based Vulnerability Detection: Are We There Yet? arXiv:2009.07235 [cs.SE] Saikat Chakraborty Rahul Krishna Yangruibo Ding and Baishakhi Ray. 2020. Deep Learning based Vulnerability Detection: Are We There Yet? arXiv:2009.07235 [cs.SE]

3. Anomaly detection

4. The Delta Maintainability Model: Measuring Maintainability of Fine-Grained Code Changes

5. Kaize Ding Jundong Li Rohit Bhanushali and Huan Liu. 2019. Deep Anomaly Detection on Attributed Networks. 594--602. https://doi.org/10.1137/1.9781611975673. 67 10.1137/1.9781611975673

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