Machine Learning Methods for Improving Vulnerability Detection in Low-level Code

被引：0

作者：

Letychevskyi, Oleksandr ^{[1
]}

Hryniuk, Yaroslav ^{[1
]}

机构：

[1] Glushkov Inst Cybernet, Kiev, Ukraine

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2020年

关键词：

symbolic modeling; graph; machine learning; node-embedding component;

D O I：

10.1109/BigData50022.2020.9377753

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a machine learning (ML) approach to improving vulnerability detection in low-level code. It uses ML classification algorithms in conjunction with novel node-embedding techniques to predict the shortest path between two nodes of a control flow graph.

引用

页码：5750 / 5752

页数：3

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