探索用于Java漏洞多标签分类的转换器

Cláudia Mamede, Eduard Pinconschi, Rui Abreu, José Campos
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引用次数: 0

摘要

深度学习(DL)技术已经证明了从高级抽象中推理脆弱代码的复杂模式的潜力。该领域的最新进展,如BERT等基于转换器的模型的引入,有助于克服可用漏洞检测数据集太小的问题,从而使大多数深度学习模型能够捕获所有相关模式。他们通过利用一般领域的知识来解决特定领域的问题来减轻挑战。在本文中,我们探索了基于bert的Java漏洞多标签分类模型。该模型的准确率高达99%,f1-score为94%。我们消除了训练数据集中的偏差,观察到f1分数下降了13%。我们进一步评估了模型在实际样本上的泛化性,并注意到其中一个模型预测未知漏洞的f1得分接近85%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring Transformers for Multi-Label Classification of Java Vulnerabilities
Deep learning (DL) techniques have demonstrated potential in reasoning complex patterns of vulnerable code from high-level abstractions. Recent advancements in the area, such as the introduction of transformer-based models, like BERT, help overcome the problem of the available vulnerability detection datasets being too small to enable most DL models to capture all relevant patterns. They mitigate the challenge by leveraging knowledge from a general domain to solve problems in specific domains. In this paper, we explore different BERT-based models for multi-label classification of vulnerabilities in Java on a synthetic dataset. The models yield up to 99% in accuracy and 94% in f1-score. We remove biases in the training dataset and observe drops of up to 13% of the f1-score. We further assess the generalizability of the models on realistic samples and notice that one model, in particular, predicted unknown vulnerabilities with an f1-score of nearly 85%.
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