Over the years, open-source software systems have become prey to threat
actors. Even as open-source communities act quickly to patch the breach, code
vulnerability screening should be an integral part of agile software
development from the beginning. Unfortunately, current vulnerability screening
techniques are ineffective at identifying novel vulnerabilities or providing
developers with code vulnerability and classification. Furthermore, the
datasets used for vulnerability learning often exhibit distribution shifts from
the real-world testing distribution due to novel attack strategies deployed by
adversaries and as a result, the machine learning model's performance may be
hindered or biased. To address these issues, we propose a joint interpolated
multitasked unbiased vulnerability classifier comprising a transformer
"RoBERTa" and graph convolution neural network (GCN). We present a training
process utilizing a semantic vulnerability graph (SVG) representation from
source code, created by integrating edges from a sequential flow, control flow,
and data flow, as well as a novel flow dubbed Poacher Flow (PF). Poacher flow
edges reduce the gap between dynamic and static program analysis and handle
complex long-range dependencies. Moreover, our approach reduces biases of
classifiers regarding unbalanced datasets by integrating Focal Loss objective
function along with SVG. Remarkably, experimental results show that our
classifier outperforms state-of-the-art results on vulnerability detection with
fewer false negatives and false positives. After testing our model across
multiple datasets, it shows an improvement of at least 2.41% and 18.75% in the
best-case scenario. Evaluations using N-day program samples demonstrate that
our proposed approach achieves a 93% accuracy and was able to detect 4,
zero-day vulnerabilities from popular GitHub repositories.

提出了一种结合 RoBERTa 模型和 GCN 模型的关于代码漏洞检测的多任务分类器，该模型采用了一个语义性漏洞图来降低偏差，并通过结合 Focal Loss 目标函数降低数据集不平衡的影响。在多个数据集的测试中，该模型表现优异，能够在最好的情况下提高 2.41% 和 18.75% 的检测效果，并且在针对知名 Github 代码库的 N-day 程序样本的测试中表现出 93% 的准确率，能够检测出 4 种零日漏洞。