Reentrancy Vulnerability Detection and Localization: A Deep Learning Based Two-phase Approach

Author:

Zhang Zhuo1ORCID,Lei Yan1,Yan Meng1,Yu Yue2,Chen Jiachi3,Wang Shangwen2,Mao Xiaoguang2

Affiliation:

1. School of Big Data & Software, Chongqing University, China

2. College of Computer, National University of Defense Technology, China

3. School of Software Engineering, Sun Yat-sen University, China

Funder

National Nature Science Foundation of China

National Key Research and Development Project of China

Natural Science Foundation of Chongqing

Major Key Project of PCL

Publisher

ACM

Reference72 articles.

1. Hervé Abdi . 2007. The Bonferonni and Šidák Corrections for Multiple Comparisons. Encyclopedia of measurement and statistics 3 ( 2007 ), 103–107. Hervé Abdi. 2007. The Bonferonni and Šidák Corrections for Multiple Comparisons. Encyclopedia of measurement and statistics 3 (2007), 103–107.

2. Miltiadis Allamanis Marc Brockschmidt and Mahmoud Khademi. 2017. Learning to represent programs with graphs. arXiv preprint arXiv:1711.00740(2017). Miltiadis Allamanis Marc Brockschmidt and Mahmoud Khademi. 2017. Learning to represent programs with graphs. arXiv preprint arXiv:1711.00740(2017).

3. Uri Alon , Shaked Brody , Omer Levy , and Eran Yahav . 2018 . code2seq: Generating Sequences from Structured Representations of Code . In Proceedings of the International Conference on Learning Representations. Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav. 2018. code2seq: Generating Sequences from Structured Representations of Code. In Proceedings of the International Conference on Learning Representations.

4. Formal Verification of Smart Contracts

5. Explainable machine learning in deployment

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