  Context: The IoT system infrastructure platform facility vulnerability attack
has become the main battlefield of network security attacks. Most of the
traditional vulnerability mining methods rely on vulnerability detection tools
to realize vulnerability discovery. However, due to the inflexibility of tools
and the limitation of file size, its scalability It is relatively low and
cannot be applied to large-scale power big data fields. Objective: The goal of
the research is to intelligently detect vulnerabilities in source codes of
high-level languages such as C/C++. This enables us to propose a code
representation of sensitive sentence-related slices of source code, and to
detect vulnerabilities by designing a distributed deep ensemble learning model.
Method: In this paper, a new directional vulnerability mining method of
parallel ensemble learning is proposed to solve the problem of large-scale data
vulnerability mining. By extracting sensitive functions and statements, a
sensitive statement library of vulnerable codes is formed. The AST stream-based
vulnerability code slice with higher granularity performs doc2vec sentence
vectorization on the source code through the random sampling module, obtains
different classification results through distributed training through the
Bi-LSTM trainer, and obtains the final classification result by voting.
Results: This method designs and implements a distributed deep ensemble
learning system software vulnerability mining system called DCDetector. It can
make accurate predictions by using the syntactic information of the code, and
is an effective method for analyzing large-scale vulnerability data.
Conclusion: Experiments show that this method can reduce the false positive
rate of traditional static analysis and improve the performance and accuracy of
machine learning.
