Research Article
Vul-Mirror: A Few-Shot Learning Method for Discovering Vulnerable Code Clone
@ARTICLE{10.4108/eai.13-7-2018.165275, author={Yuan He and Wenjie Wang and Hongyu Sun and Yuqing Zhang}, title={Vul-Mirror: A Few-Shot Learning Method for Discovering Vulnerable Code Clone}, journal={EAI Endorsed Transactions on Security and Safety}, volume={7}, number={23}, publisher={EAI}, journal_a={SESA}, year={2020}, month={6}, keywords={Vulnerability, few-shot learning, code clone, distance-metric}, doi={10.4108/eai.13-7-2018.165275} }- Yuan He
Wenjie Wang
Hongyu Sun
Yuqing Zhang
Year: 2020
Vul-Mirror: A Few-Shot Learning Method for Discovering Vulnerable Code Clone
SESA
EAI
DOI: 10.4108/eai.13-7-2018.165275
Abstract
It is quite common for reusing code in soft development, which may lead to the wide spread of the vulnerability, so automatic detection of vulnerable code clone is becoming more and more important. However, the existing solutions either cannot automatically extract the characteristics of the vulnerable codes or cannot select different algorithms according to different codes, which results in low detection accuracy. In this paper, we consider the identification of vulnerable code clone as a code recognition task and propose a method named Vul-Mirror based on a few-shot learning model for discovering clone vulnerable codes. It can not only automatically extract features of vulnerabilities, but also use the network to measure similarity. The results of experiments on open-source projects of five operating systems show that the accuracy of Vul-Mirror is 95.7%, and its performance is better than the state-of-the-art methods.
Copyright © 2020 Yuan He et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
