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BBDetector: Intelligent border binary detection in IoT device firmware based on a multidimensional feature model.

Shudan Yue1,2, Guimin Zhang1,2, Qingbao Li1

  • 1Information Engineering University, Zhengzhou, China.

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Summary
This summary is machine-generated.

BBDetector enhances Internet of Things (IoT) firmware security by introducing a multidimensional feature model for border binary detection. This method significantly improves accuracy and reduces false negatives in identifying critical binaries.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Existing border binary detection methods for IoT firmware suffer from poor feature characterization, high false-negative rates, and low intelligence.
  • Accurate border binary detection is crucial for effective IoT firmware security analysis and vulnerability identification.

Purpose of the Study:

  • To develop an advanced border binary detection method for IoT firmware security analysis.
  • To address limitations of current methods by improving feature characterization and reducing false-negative rates.

Main Methods:

  • Constructed a novel, large-scale dataset of border binaries from diverse real-world IoT firmware.
  • Proposed a multidimensional feature model (MDFM) for comprehensive feature extraction.
  • Developed a stacking ensemble learning model (XLC-R) combining gradient boosting variants and random forest for detection.

Main Results:

  • The XLC-R model achieved high performance on Dataset I with 94.98% precision, 91.02% recall, and 92.84% F1 score.
  • BBDetector identified significantly more border binaries in Dataset II compared to state-of-the-art tools (3.25x Karonte, 2.23x SaTC).

Conclusions:

  • BBDetector offers an accurate and intelligent solution for border binary detection in IoT firmware.
  • The method enhances vulnerability detection relevance, simplifies firmware analysis, and supports improved IoT device security.