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Automated Vulnerability Detection in Source Code Using Minimum Intermediate Representation

Vulnerability is one of the root causes of network intrusion. An effective way to mitigate security threats is to discover and patch vulnerabilities before an attack. Traditional vulnerability detection methods rely on manual participation and incur a high false positive rate. The intelligent vulnerability detection methods suffer from the problems of long-term dependence, out of vocabulary, coarse detection granularity and lack of vulnerable samples.
This paper proposes an automated and intelligent vulnerability detection method in source code based on the minimum intermediate representation learning.
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