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Area of Science:

  • Computer Science
  • Software Engineering
  • Cybersecurity

Background:

  • The open-source community accelerates software development but increases vulnerability spread, particularly in Internet of Things (IoT) devices.
  • Existing firmware security inspection methods struggle with unknown vulnerabilities and suffer from low efficiency and poor extensibility due to extensive training data requirements.
  • The increasing frequency and severity of attacks on connected devices necessitate advanced vulnerability detection techniques.

Purpose of the Study:

  • To propose a high-efficiency similarity analysis approach for firmware code to address the limitations of current security inspection technologies.
  • To enable large-scale firmware function similarity analysis and locate real-world firmware patches without prior vulnerability function information.
  • To improve the efficiency and accuracy of firmware vulnerability detection compared to existing methods.

Main Methods:

  • Extract function control flow and data flow features from firmware and vulnerability functions to compute SimHash for efficient similarity calculation.
  • Utilize the pigeonhole principle for mass storage and fast querying of SimHash values.
  • Analyze similarity within and among basic blocks using symbolic execution, constraint solvers for semantic equivalence, and local control flow graphs.

Main Results:

  • The proposed approach successfully implements large-scale firmware function similarity analysis.
  • The method can accurately locate real-world firmware patches even without specific vulnerability function details.
  • Evaluation demonstrates superior efficiency and accuracy compared to Gemini and StagedMethod, with over 90% of firmware functions indexed within 0.1s and searches completed in under 2s for 100,000 functions.

Conclusions:

  • The developed SimHash-based approach offers a highly efficient and accurate solution for firmware similarity analysis and vulnerability detection.
  • This method significantly enhances the ability to secure IoT devices by enabling rapid identification of potential security flaws.
  • The approach overcomes the limitations of existing methods, providing a scalable and effective tool for cybersecurity professionals.