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A Detection Framework of Malicious Code Based on Multi-Classifiers Ensemble

초록

영어

Malicious code detection is one of the important missions of malicious code analysis. Current researches on the detection of malicious code mostly focused on single classifier, whereas the single classifier is not suitable for the detection based on features of different types. We utilized multi-classifiers ensemble based on fuzzy integral to improve the accuracy of the detection framework. A framework based on the Choquet fuzzy integral was proposed to fuse the analysis results of the base classifiers with different features. And the genetic algorithm was used to obtain the fuzzy measure. Finally, the result of Choquet fuzzy integral was compared to a threshold predefined to determine the maliciousness of binary code. Experiment showed that the framework proposed in this paper could be used to determine the maliciousness of binary code more accurately.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Basic of Fuzzy Integral
  3.1. Fuzzy Measure
  3.2. Fuzzy Integral
 4. Detection Framework Based on Multi-classifiers Ensemble
  4.1. Overview of the Detection Framework
  4.2. Architecture of Static Analysis
  4.3. Fuzzy Measure Calculation based on Genetic Algorithm
 5. Experiments and Results
 6. Conclusion and Future Work
 Acknowledgments
 References

저자정보

  • Chao Dai State Key Lab of Mathematical Engineering and Advanced Computing of China
  • Jianmin Pang State Key Lab of Mathematical Engineering and Advanced Computing of China
  • Feng Yue State Key Lab of Mathematical Engineering and Advanced Computing of China
  • Pingfei Cui State Key Lab of Mathematical Engineering and Advanced Computing of China
  • Di Sun State Key Lab of Mathematical Engineering and Advanced Computing of China
  • Liang Zhu State Key Lab of Mathematical Engineering and Advanced Computing of China

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