一个分层的深度学习框架,具有双重规范化的损失,用于强大的恶意软件检测和家族分类
Shatha Abed Alsaedi1, Majed Alwateer1, Nouf Helal Alharbi2
1Department of Computer Science, College of Computer Science and Engineering, Taibah University, 46421, Yanbu, Saudi Arabia.
Scientific reports
|January 6, 2026
概括
这项研究引入了一个新的深度学习框架,用于检测和分类便携式可执行 (PE) 恶意软件. 它使用一种新的损失函数来提高网络安全威胁检测的准确性和稳定性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 越来越多的网络威胁需要先进的恶意软件检测.
- 传统的深度学习方法与阶级不平衡和过度拟合作斗争.
- 需要具有数学基础和可解释的检测框架.
研究的目的:
- 为便携式可执行 (PE) 恶意软件检测和家族分类提出一个层次化的深度学习框架.
- 介绍一个新的双重调节二元交叉 (DRBCE) 损失函数.
- 解决诸如阶级不平衡,过度装配和错误传播等挑战.
主要方法:
- 开发了一个两阶段的层次深度学习框架.
- 利用了一种新的双重调节二元交叉 (DRBCE) 损失函数,集成加权交叉和光谱调节.
- 使用概率框架模拟错误传播,并通过受约束优化优化超参数.
- 在大型数据集 (BODMAS,SOMLAP,CLaMP) 上进行实验.
主要成果:
- 在恶意软件检测和家族分类方面取得了最先进的性能.
- 对二进制检测的加权精度达到99.89%,对家族分类的加权精度达到96.62%.
- 证明了DRBCE损失函数在稳定训练和减轻不平衡方面的有效性.
结论:
- 拟议的框架提供了强大的和可解释的恶意软件检测.
- 成功地将数学建模与应用AI在网络安全中的桥梁.
- 提供理论见解和实践进展,用于打击复杂的恶意软件.
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