一种改进的软件源代码漏洞检测方法:多功能选和集成采样模型的组合
1College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
这项研究引入了软件漏洞检测的新模型,提高了准确性并缩短了培训时间. 多特征选和综合采样模型 (MFISM) 通过解决数据集不平衡,提高软件安全性.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
- 网络安全 网络安全
背景情况:
- 软件漏洞检测对于安全至关重要.
- 现有的方法在不平衡的数据集和漫长的培训中扎.
- 需要高效准确的漏洞检测模型.
研究的目的:
- 引入多特征选和综合采样模型 (MFISM) 以加强软件漏洞检测.
- 提高源代码中识别漏洞的效率和准确性.
- 为了克服现有模型中的阶级不平衡和长时间培训的挑战.
主要方法:
- 使用抽象语法树 (AST) 进行特征提取.
- 应用变异分析 (ANOVA) 和特征选择技术.
- 实现了数据平衡的综合过量采样和异常结果检测.
- 采用双向长期短期记忆 (Bi-LSTM) 网络进行分类.
主要成果:
- 与DeepBalance相比,MFISM提高了F1的得分约10%.
- 将模型培训时间缩短到2-3小时.
- 在源代码漏洞检测方面表现出卓越的性能.
结论:
- MFISM有效地提高了漏洞检测的准确性和效率.
- 该模型成功地解决了阶级不平衡,并减少了培训时间.
- MFISM为安全软件开发提供了卓越的解决方案.
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