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IceShield: Detection and Mitigation of Malicious Websites with a Frozen DOM

  • Mario Heiderich
  • Tilman Frosch
  • Thorsten Holz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6961)

Abstract

Due to its flexibility and dynamic character, JavaScript has become an important tool for attackers. The widespread scripting language often helps them to perform a broad variety of malicious activities, for example to initiate drive-by download exploits or to execute clickjacking attacks. Current defense mechanisms as well as reactive analysis and forensic approaches are often slow or complicated to set up and conduct since an attacker can use many different ways to obfuscate the code or make it hard to obtain a copy of the code in the first place.

In this paper, we introduce a novel approach to analyze this class of attacks by demonstrating how dynamic analysis of websites can be accomplished directly in the browser. We present IceShield, a JavaScript based tool that enables in-line dynamic code analysis as well as de-obfuscation, and a set of heuristics to detect attempts of attacking either a website or the user accessing its contents. Special care needs to be taken to implement the instrumentation in a robust and tamper resistant way since an attacker should not be able to bypass our detection process. We show how features of ECMA Script 5 can be used to freeze object properties, so they cannot be modified during runtime. We implemented a prototype version of IceShield and demonstrate that it detects malicious websites with a small overhead even on devices with limited computing power such as smartphones. Furthermore, IceShield can mitigate detected attacks by changing suspicious elements, so they do not cause harm anymore, thus actually protecting users from such attacks.

Keywords

Linear Discriminant Analysis User Agent Malicious Code Window Context Attack Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mario Heiderich
    • 1
  • Tilman Frosch
    • 1
  • Thorsten Holz
    • 1
  1. 1.Chair for Network and Data SecurityRuhr-UniversityBochumGermany

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