Identifying evasive code in maliciouswebsites by analyzing redirection differences

Yuta Takata, Mitsuaki Akiyama, Takeshi Yagi, Takeo Hariu, Kazuhiko Ohkubo, Shigeki Goto

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)


    Security researchers/vendors detect malicious websites based on several website features extracted by honeyclient analysis. However, web-based attacks continue to be more sophisticated along with the development of countermeasure techniques. Attackers detect the honeyclient and evade analysis using sophisticated JavaScript code. The evasive code indirectly identifies vulnerable clients by abusing the differences among JavaScript implementations. Attackers deliver malware only to targeted clients on the basis of the evasion results while avoiding honeyclient analysis. Therefore, we are faced with a problem in that honeyclients cannot analyze malicious websites. Nevertheless, we can observe the evasion nature, i.e., the results in accessing malicious websites by using targeted clients are different from those by using honeyclients. In this paper, we propose a method of extracting evasive code by leveraging the above differences to investigate current evasion techniques. Our method analyzes HTTP transactions of the same website obtained using two types of clients, a real browser as a targeted client and a browser emulator as a honeyclient. As a result of evaluating our method with 8,467 JavaScript samples executed in 20,272 malicious websites, we discovered previously unknown evasion techniques that abuse the differences among JavaScript implementations. These findings will contribute to improving the analysis capabilities of conventional honeyclients.

    Original languageEnglish
    Pages (from-to)2600-2611
    Number of pages12
    JournalIEICE Transactions on Information and Systems
    Issue number11
    Publication statusPublished - 2018 Nov 1


    • Evasive code
    • Javascript
    • Malicious website
    • Redirection

    ASJC Scopus subject areas

    • Software
    • Hardware and Architecture
    • Computer Vision and Pattern Recognition
    • Electrical and Electronic Engineering
    • Artificial Intelligence


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