Earticle

초록

영어

The sheer volume of new malware found each day is growing at an exponential pace. Centralized systems that collect all malware samples to central severs can cause problems of single point of failure as well as processing bottlenecks. Previous works on distributed and scalable malware analysis are mainly applied for specific or simple malware. This paper presents CCS, a collaborative online malware analysis system which is applied for various malware and well scalable. Each sensors in CCS analysis their own malware samples accurately in-situ and then CCS aggregates those analyses among sensors in a load-balance way. We implemented a proof-of-concept version of CCS and performed experiments with 917 real-world malware samples; preliminary results from our evaluation confirm that CCS has comparable performance with centralized system, but much better scalability, and is approximately consistent with the result of AV scanners.

목차

Abstract
 1. Introduction
 2. Overview of CCS Malware Analysis Framework
  2.1 Architecture of CCS
  2.2 INFOACC (information-driven accumulation structure)
  2.3 Local Analysis Stage
  2.4 Global Aggregation Stage
 3. Evaluation
  3.1 Experimental Setup
  3.2 Comparing with Centralized System and AV Scanners
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Huabiao Lu [ School of Computer, National University of Defense Technology ]
• Xiaofeng Wang [ School of Computer, National University of Defense Technology ]
• Jinshu Su [ School of Computer, National University of Defense Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605