公开(公告)号：US20240202099A1

公开(公告)日：2024-06-20

申请号：US18368833

申请日：2023-09-15

申请人： EWHA UNIVERSITY-INDUSTRY COLLABORATION FOUNDATION

发明人： Byoung Ju CHOI ,  Ji Hyun PARK

IPC分类号： G06F11/36

CPC分类号： G06F11/3612 ,  G06F11/3624 ,  G06F11/3636

摘要： Provided are a device and method for detecting a variable vulnerability in software using a machine learning (ML) model. The method performed by an analysis device includes receiving a source code of a program to be analyzed, replacing call functions, variable names, and call stack functions in an execution log generated during execution of the source code with certain identifiers (IDs) to preprocess the execution log, analyzing the preprocessed execution log through a pretrained first learning model to classify whether each pair of a global variable and a call function is at an initialization location, analyzing the preprocessed execution log through a pretrained second learning model to estimate a maximum value and a minimum value of the global variable, and determining whether the global variable is vulnerable on the basis of information output by the first learning model and information output by the second learning model.