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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

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Double-Layer Detection Model of Malicious PDF Documents Based on Entropy Method with Multiple Features.

Enzhou Song¹, Tao Hu¹, Peng Yi¹

¹Information Technology Institute, Information Engineering University, Zhengzhou 450001, China.

Entropy (Basel, Switzerland)

|July 29, 2023

Summary

This summary is machine-generated.

This study introduces a novel double-layer model for detecting malicious PDF documents. It effectively identifies threats using a fusion of 222 features and an entropy method, achieving high accuracy and speed.

Keywords:

AdaBoost-optimized random forest algorithm PDF document detection entropy method multiple features robustness-optimized support vector machine algorithm

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Area of Science:

Computer Science
Cybersecurity
Machine Learning

Background:

Traditional PDF malware detection relies on specific rules, limiting its effectiveness against diverse and evolving threats.
Existing methods struggle with advanced evasion techniques like obfuscation, encryption, and imitation attacks.

Purpose of the Study:

To develop a robust and efficient double-layer detection model for malicious PDF documents.
To overcome the limitations of single-target detection and enhance resistance against anti-detection strategies.

Main Methods:

A comprehensive feature set of 222 elements (130 basic, 82 dangerous) was fused to resist obfuscation and encryption.
An entropy method based on RReliefF and MIC (EMBORAM) was employed to select an optimal feature subset of 153 for enhanced anti-evasion.
A dual-layer framework utilized AdaBoost-optimized random forest and robustness-optimized support vector machine algorithms for efficient detection.

Main Results:

The proposed model demonstrated superior performance across various evaluation metrics compared to traditional static detection methods.
Achieved an impressive average detection time of 1.3 milliseconds.
Reached a high accuracy rate of 95.9% in identifying malicious PDF documents.

Conclusions:

The developed double-layer detection model offers a significant advancement in identifying malicious PDF files.
The fusion of extensive features and advanced entropy-based selection enhances resilience against sophisticated evasion tactics.
The model provides a fast and accurate solution for real-time PDF malware detection.