Alexander Pechenkin
ABSTRACT
This paper 1 addresses a problem of vulnerability detection in software represented as assembly code. An extended approach to the vulnerability detection problem is proposed. This work concentrates on improvement of neural network-based approach described in previous works of authors. The authors propose to include the morphology of instructions in vector representations. The bidirectional recurrent neural network is used with access to the execution traces of the program. This has significantly improved the vulnerability detecting accuracy.
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Index Terms
- Applying Deep Learning and Vector Representation for Software Vulnerabilities Detection
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