基于双层BiLSTM的安装程序DLL劫持漏洞挖掘方法
Journal of Software（2023）
南京邮电大学
Abstract
动态链接库(dynamic link library,DLL)的出现给开发人员提供了极大的便利,也提高了操作系统与应用程序之间的交互性.然而,动态链接库本身存在的安全性隐患不容忽视,如何有效地挖掘Windows平台下安装程序执行过程中出现的DLL劫持漏洞是当下保障Windows操作系统安全的关键问题之一.搜集并提取大量安装程序的属性特征,从安装程序、安装程序调用DLL模式、DLL文件本身 3 个角度出发,使用双层BiLSTM(bi-directional long short-term memory)神经网络进行学习,抽取出漏洞数据集的多维特征,挖掘DLL劫持未知漏洞.实验可有效检测Windows平台下安装程序的DLL劫持漏洞,共挖掘 10 个未知漏洞并获得CNVD漏洞授权,此外通过和其他漏洞分析工具进行对比进一步验证该方法的有效性和完整性.
MoreKey words
vulnerability mining,neural network,dynamic link library(DLL)
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