Detection technology of malicious code based on semantic

被引:4
|
作者
Lu, Qingmei [1 ,2 ,3 ]
Wang, Yulin [1 ]
机构
[1] Wuhan Univ, Int Sch Software, Wuhan 430072, Hubei, Peoples R China
[2] North Univ China, Sch Sci & Control Engn, Taiyuan 030051, Shanxi, Peoples R China
[3] Univ Louisville, Dept Bioengn, Louisville, KY 40292 USA
关键词
Semantic; Malicious code; Detection; Characteristic extraction;
D O I
10.1007/s11042-015-3228-7
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper puts forward one kind of behavioral characteristic extraction and detection method of malicious code based on semantic; it extracts the key behavior and dependence relations among behaviors by combining with stain spread analysis in command layer and semantic analysis in behavior layer. And then it uses anti-confusion engine identification semantic irrelevance and semantic equivalence behavior to obtain malicious code behavior characteristic with certain capacity of resisting disturbance, as well as realize characteristic extraction and detection on prototype system. It completes experimental demonstration on this system through analysis and detection on plenty of malicious code samples. The test result indicates that extraction characteristic based on the above methods has characteristic such as stronger capacity of resisting disturbance etc., detection based on this characteristic has better identification ability for malicious code.
引用
收藏
页码:19543 / 19555
页数:13
相关论文
共 50 条
  • [1] Detection technology of malicious code based on semantic
    Qingmei Lu
    Yulin Wang
    [J]. Multimedia Tools and Applications, 2017, 76 : 19543 - 19555
  • [2] Malicious Code Detection Based on Code Semantic Features
    Zhang, Yu
    Li, Binglong
    [J]. IEEE ACCESS, 2020, 8 : 176728 - 176737
  • [3] Malicious code clone detection technology based on deep learning
    Shen, Yuan
    Yan, Hanbing
    Xia, Chunhe
    Han, Zhihui
    [J]. Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics, 2022, 48 (02): : 282 - 290
  • [4] A Recurrent Neural Network-based Malicious Code Detection Technology
    Tang, Yongwang
    Liu, Xin
    Jin, Yanqing
    Wei, Han
    Deng, Qizheng
    [J]. PROCEEDINGS OF 2019 IEEE 8TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC 2019), 2019, : 1737 - 1742
  • [5] COMPUTER MALICIOUS CODE SIGNAL DETECTION BASED ON BIG DATA TECHNOLOGY
    Liu, Xiaoteng
    [J]. SCALABLE COMPUTING-PRACTICE AND EXPERIENCE, 2023, 24 (03): : 521 - 530
  • [6] Malicious Code Detection Technology Based on A3C Algorithm
    Xue, Yi
    Shu, Hui
    Bu, Wenjuan
    Qu, Wu
    [J]. PROCEEDINGS OF 2020 IEEE 11TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS 2020), 2020, : 116 - 120
  • [7] Static Detection of Malicious Code in Programs Using Semantic Techniques
    Navid, Syed Zami-Ul-Haque
    Dey, Protik
    Hasan, Shamiul
    Ali, Muhammad Masroor
    [J]. PROCEEDINGS OF 2020 11TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (ICECE), 2020, : 327 - 330
  • [8] Detecting malicious Java']JavaScript code based on semantic analysis
    Fang, Yong
    Huang, Cheng
    Su, Yu
    Qiu, Yaoyao
    [J]. COMPUTERS & SECURITY, 2020, 93
  • [9] Malicious Code Detection Based on Software Fingerprint
    Yin, Zhimin
    Yu, Xiangzhan
    Niu, Linhua
    [J]. PROCEEDINGS OF THE 2013 THE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND SOFTWARE ENGINEERING (ICAISE 2013), 2013, 37 : 212 - 216
  • [10] Unknown Malicious Code Detection Based on Bayesian
    Lai, Yingxu
    Liu, Zhenghui
    [J]. CEIS 2011, 2011, 15