使用预训练的变压器编码器和字节序列的恶意软件检测
Eun-Jin Kim1, Yun-Kyung Lee2, Sang-Min Lee2
1Department of Computer Engineering, Chungbuk National University, Cheongju, Chungcheongbukdo, Republic of Korea.
PloS one
|October 13, 2025
概括
本研究介绍了一种基于变压器的新型语言模型,用于使用字节序列增强恶意软件检测. 我们的模型显著优于卷积神经网络,提高了检测准确度.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 恶意攻击正在不断发展,需要先进的恶意软件检测方法.
- 神经网络,特别是卷积神经网络 (CNN),用于分析文件字节序列.
- 基于变压器的语言模型在各种领域显示出希望,并且可以适应网络安全任务.
研究的目的:
- 引入一种新的基于变压器的语言模型,用于使用文件字节序列检测恶意软件.
- 在这个领域,为变压器模型提出和评估新的预培训策略.
- 为预训练和微调恶意软件检测模型建立一个基准数据集.
主要方法:
- 开发了一个基于变压器的语言模型来处理文件字节序列.
- 引入了两种新的预训策略:真假预测和相同序列预测.
- 探索了预训练策略的组合,包括掩面语言建模和下一个句子的预测.
- 创建了一个基准数据集,包括PDF,HWP和MS Office文件.
主要成果:
- 拟议的基于变压器的语言模型在恶意软件检测方面表现优于CNN.
- 与CNN相比,实现了宏观F1得分改进,从2.7%p到11.1%p不等.
- 经验结果验证了新型预培训策略的有效性.
结论:
- 基于变压器的语言模型为恶意软件检测提供了比CNN更好的方法.
- 开发的模型和预训练策略为未来的恶意软件检测服务提供了坚实的基础.
- 未来的工作重点是开发基于编码器的模型来处理更长的字节序列.
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