恶意PDF文档的双层检测模型基于具有多个特征的透法
Enzhou Song1, Tao Hu1, Peng Yi1
1Information Technology Institute, Information Engineering University, Zhengzhou 450001, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种新的双层模型,用于检测恶意PDF文档. 它有效地使用222个特征和方法的融合来识别威胁,实现高精度和速度.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 传统的PDF恶意软件检测依赖于特定的规则,限制其对不同和不断变化的威胁的有效性.
- 现有的方法与先进的逃避技术 (如模糊,加密和模仿攻击) 斗争.
研究的目的:
- 开发一个强大而高效的双层检测模型来检测恶意PDF文档.
- 克服单个目标检测的局限性,增强抗检测策略的抵抗力.
主要方法:
- 一个包括222个元素 (130个基本元素,82个危险元素) 的综合功能集被融合在一起,以抵御模糊和加密.
- 基于RReliefF和MIC (EMBORAM) 的方法被采用,以选择153的最佳特征子集,以增强反逃避.
- 一个双层框架使用了AdaBoost优化的随机森林和强度优化的支持矢量机算法,以实现高效的检测.
主要成果:
- 与传统的静态检测方法相比,拟议的模型在各种评估指标上表现出卓越的性能.
- 取得了令人印象深刻的1.3毫秒的平均检测时间.
- 在识别恶意PDF文档方面达到95.9%的高准确率.
结论:
- 开发的双层检测模型在识别恶意PDF文件方面取得了重大进展.
- 广泛的特征和先进的基于的选择的融合增强了对复杂的逃避策略的弹性.
- 该模型为实时PDF恶意软件检测提供了快速而准确的解决方案.
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