Details

Autor(en) / Beteiligte

• Zhu, Yuhui
• Lin, Guanjun
• Song, Lipeng
• Zhang, Jun

Titel

The application of neural network for software vulnerability detection: a review

Ist Teil von

• Neural computing & applications, 2023, Vol.35 (2), p.1279-1301

Ort / Verlag

London: Springer London

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• To date, being benefited from the ability of automated feature extraction and the performance of software vulnerability identification, deep learning techniques have attracted extensive attention in data-driven software vulnerability detection. Many methods based on deep learning have been proposed to speed up and intelligentize the process of vulnerability identification. Although these methods have shown significant advantages over traditional machine learning ones, there is an apparent gap between the deep learning-based detection systems and human experts in understanding potentially vulnerable code semantics. In some real-world vulnerability prediction scenarios, the performance of deep learning-based methods drops by more than 50% compared to these methods’ performance in experimental scenarios. We define this phenomenon as the perception gap by examining and reviewing the early software vulnerability detection approaches. Then, from the perspective of the perception gap, this paper profoundly explores the current software vulnerability detection methods and how existing solutions endeavor to narrow the perception gap and push forward the development of the field of interest. Finally, we summarize the challenges of this new field and discuss the possible future.