@Article{cmc.2020.09345,
AUTHOR = {Zhongxu Yin, Yiran Song, Huiqin Chen, Yan Cao},
TITLE = {A Security Sensitive Function Mining Approach Based on  Precondition Pattern Analysis},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {2},
PAGES = {1013--1029},
URL = {http://www.techscience.com/cmc/v63n2/38557},
ISSN = {1546-2226},
ABSTRACT = {Security-sensitive functions are the basis for building a taint-style vulnerability 
model. Current approaches for extracting security-sensitive functions either don’t analyze 
data flow accurately, or not conducting pattern analyzing of conditions, resulting in 
higher false positive rate or false negative rate, which increased manual confirmation 
workload. In this paper, we propose a security sensitive function mining approach based 
on preconditon pattern analyzing. Firstly, we propose an enhanced system dependency 
graph analysis algorithm for precisely extracting the conditional statements which check 
the function parameters and conducting statistical analysis of the conditional statements 
for selecting candidate security sensitive functions of the target program. Then we adopt 
a precondition pattern mining method based on conditional statements nomalizing and 
clustering. Functions with fixed precondition patterns are regarded as security-sensitive 
functions. The experimental results on four popular open source codebases of different 
scales show that the approach proposed is effective in reducing the false positive rate and 
false negative rate for detecting security sensitive functions.},
DOI = {10.32604/cmc.2020.09345}
}