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清华大学学报(自然科学版)  2016, Vol. 65 Issue (5): 484-492    DOI: 10.16511/j.cnki.qhdxxb.2016.25.005
  信息安全 本期目录 | 过刊浏览 | 高级检索 |
基于离线汇编指令流分析的恶意程序算法识别技术
赵晶玲1,2, 陈石磊1,2, 曹梦晨1,2, 崔宝江1,2
1. 北京邮电大学 计算机学院, 北京 100876;
2. 移动互联网安全技术国家工程实验室, 北京 100876
Malware algorithm recognition based on offline instruction-flow analyse
ZHAO Jingling1,2, CHEN Shilei1,2, CAO Mengchen1,2, CUI Baojiang1,2
1. School of Computer, Beijng University of Post and Telecommunications, Beijing 100876, China;
2. National Engineering Lab for Mobile Network Security, Beijing 100876, China
全文: PDF(1148 KB)  
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摘要 识别二进制程序中的算法, 在恶意程序检测、软件分析、网络传输分析、计算机系统安全保护等领域有着广泛的应用和重要的意义。该文提出基于离线汇编指令流分析的恶意代码算法识别技术, 综合运用二进制插桩、污点跟踪、循环识别等技术, 从行为语义、关键常数2个维度对程序进行描述, 并且分析提取特征。算法识别模型使用机器学习算法, 针对双维度特征生成初阶识别模型, 并通过模型融合优化识别效果, 实现对广义程序算法的高准确率识别。
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赵晶玲
陈石磊
曹梦晨
崔宝江
关键词 算法识别污点跟踪机器学习恶意程序检测    
Abstract:Binary program algorithm identification is widely used for malware detection, software analyse, network encryption analyse and computer system protection. This paper describes a malware algorithm recognition method using offline instruction-flow analyses using binary instrumentation, taint traces, and loop recognition. The algorithm features are described including the behavior semantics and key constants extracted from the instruction-flow algorithm. Two machine learning models trained by these features are merged into one accurate recognition algorithm.
Key wordsalgorithm recognition    taint trace    machine learning    malware detection
收稿日期: 2016-01-24      出版日期: 2016-05-19
ZTFLH:  TP301.6  
引用本文:   
赵晶玲, 陈石磊, 曹梦晨, 崔宝江. 基于离线汇编指令流分析的恶意程序算法识别技术[J]. 清华大学学报(自然科学版), 2016, 65(5): 484-492.
ZHAO Jingling, CHEN Shilei, CAO Mengchen, CUI Baojiang. Malware algorithm recognition based on offline instruction-flow analyse. Journal of Tsinghua University(Science and Technology), 2016, 65(5): 484-492.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2016.25.005  或          http://jst.tsinghuajournals.com/CN/Y2016/V65/I5/484
  图1 离线分析框架流程
  图2 循环结构控制流示意
  表1 循环嵌套后向搜索算法
  图3 算法识别模型融合过程示意
  表2 行为语义轮廓初阶识别模型结果
  表3 关键常数初阶识别模型结果
  表4 仲裁模型的函数算法功能识别结果
  图4 模型测试结果对比
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