Soft Computing

, Volume 18, Issue 2, pp 267–284 | Cite as

IT2FS-based ontology with soft-computing mechanism for malware behavior analysis

  • Hsien-De Huang
  • Chang-Shing Lee
  • Mei-Hui Wang
  • Hung-Yu Kao
Methodologies and Application

Abstract

Antimalware application is one of the most important research issues in the area of cyber security threat. Nowadays, because hackers continuously develop novel techniques to intrude into computer systems for various reasons, many security researchers should analyze and track new malicious program to protect sensitive and valuable information in the organization. In this paper, we propose a novel soft-computing mechanism based on the ontology model for malware behavioral analysis: Malware Analysis Network in Taiwan (MAN in Taiwan, MiT). The core techniques of MiT contain two parts listed as follows: (1) collect the logs of network connection, registry, and memory from the operation system on the physical-virtual hybrid analysis environment to get and extract more unknown malicious behavior information. The important information is then extracted to construct the ontology model by using the Web Ontology Language and Fuzzy Markup Language. Additionally, MiT is also able to automatically provide and share samples and reports via the cloud storage mechanism; (2) apply the techniques of Interval Type-2 Fuzzy Set to construct the malware analysis domain knowledge, namely the Interval Type-2 Fuzzy Malware Ontology (IT2FMO), for malware behavior analysis. Simulation results show that the proposed approach can effectively execute the malware behavior analysis, and the constructed system has also released under GNU General Public License version 3. In the future, the system is expected to largely collect and analyze malware samples for providing industries or universities to do related applications via the established IT2FMO.

Keywords

Malware behavioral analysis Type-2 fuzzy set Ontology Fuzzy markup language Soft computing 

Notes

Acknowledgments

The authors would like to thank National Science Council in Taiwan for its financial support under the grant NSC 101-2221-E-024-025. The authors also would like to thank Dept. Information and Learning Technology, National University of Tainan in Taiwan, National Cheng Kung University in Taiwan, and Acer eDC company in Taiwan for their kindly support with the Open Source research project MiT.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hsien-De Huang
    • 1
  • Chang-Shing Lee
    • 2
  • Mei-Hui Wang
    • 2
  • Hung-Yu Kao
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Cheng Kung UniversityTainan CityTaiwan
  2. 2.Department of Computer Science and Information EngineeringNational University of TainanTainan CityTaiwan

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