The De-Obfuscation Method in the Static Detection of Malicious PDF Documents

被引:0
|
作者
Wang, Yuntao [1 ]
机构
[1] University of Chinese Academy of Sciences, Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, Beijing, Beijing, China
关键词
Code obfuscation - [!text type='Java']Java[!/text] scripts - Malicious [!text type='java']java[!/text] script - Malware detection - Malware propagation - Platform independent - Portable document format document - Portable document format files - Portable document formats - Static detections;
D O I
暂无
中图分类号
学科分类号
摘要
10
引用
收藏
页码:44 / 47
相关论文
共 50 条
  • [31] DRLDO: A Novel DRL based De-obfuscation System for Defence Against Metamorphic Malware
    Sewak, Mohit
    Sahay, Sanjay K.
    Rathore, Hemant
    DEFENCE SCIENCE JOURNAL, 2021, 71 (01) : 55 - 65
  • [32] A Deep Learning-based Formula Detection Method for PDF Documents
    Gao, Liangcai
    Yi, Xiaohan
    Liao, Yuan
    Jiang, Zhuoren
    Yan, Zuoyu
    Tang, Zhi
    2017 14TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR), VOL 1, 2017, : 553 - 558
  • [33] A Table Detection Method for PDF Documents Based on Convolutional Neural Networks
    Hao, Leipeng
    Gao, Liangcai
    Yi, Xiaohan
    Tang, Zhi
    PROCEEDINGS OF 12TH IAPR WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS, (DAS 2016), 2016, : 287 - 292
  • [34] An Evasion Resilient Approach to the Detection of Malicious PDF Files
    Maiorca, Davide
    Ariu, Davide
    Corona, Igino
    Giacinto, Giorgio
    INFORMATION SYSTEMS SECURITY AND PRIVACY, ICISSP 2015, 2015, 576 : 68 - 85
  • [35] FEPDF: A Robust Feature Extractor for Malicious PDF Detection
    Li, Min
    Liu, Yunzheng
    Yu, Min
    Li, Gang
    Wang, Yongjian
    Liu, Chao
    2017 16TH IEEE INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS / 11TH IEEE INTERNATIONAL CONFERENCE ON BIG DATA SCIENCE AND ENGINEERING / 14TH IEEE INTERNATIONAL CONFERENCE ON EMBEDDED SOFTWARE AND SYSTEMS, 2017, : 218 - 224
  • [36] DETECTING MALICIOUS PDF DOCUMENTS USING SEMI-SUPERVISED MACHINE LEARNING
    Jiang, Jianguo
    Song, Nan
    Yu, Min
    Chow, Kam-Pui
    Li, Gang
    Liu, Chao
    Huang, Weiqing
    ADVANCES IN DIGITAL FORENSICS XVII, 2021, 612 : 135 - 155
  • [37] Malicious PDF Detection using Metadata and Structural Features
    Smutz, Charles
    Stavrou, Angelos
    28TH ANNUAL COMPUTER SECURITY APPLICATIONS CONFERENCE (ACSAC 2012), 2012, : 239 - 248
  • [38] Research and Improvement of Feature Engineering for Malicious PDF Detection
    Huang N.
    He J.
    Wu Y.
    Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China, 2022, 51 (05): : 766 - 773
  • [39] Malicious PDF document detection based on mixed feature
    Du X.
    Lin Y.
    Sun Y.
    Tongxin Xuebao/Journal on Communications, 2019, 40 (02): : 118 - 128
  • [40] Warm Up before Circuit De-obfuscation? An Exploration through Bounded-Model-Checkers
    Azar, Kimia Zamiri
    Kamali, Hadi Mardani
    Farahmandi, Farimah
    Tehranipoor, Mark
    2022 IEEE INTERNATIONAL SYMPOSIUM ON HARDWARE ORIENTED SECURITY AND TRUST (HOST), 2022, : 13 - 16