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Research on the Classification Model of Similarity Malware using Fuzzy Hash
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퍼지해시를 이용한 유사 악성코드 분류모델에 관한 연구

논문 기본 정보

Type
Academic journal
Author
Changwook Park (고려대학교) Hyunji Chung (고려대학교) Kwangseok Seo (한국정보보호교육센터) Sangjin Lee (고려대학교)
Journal
Korea Institute Of Information Security And Cryptology Journal of the Korea Institute of Information Security & Cryptology Vol.22 No.6 KCI Accredited Journals
Published
2012.12
Pages
1,325 - 1,336 (12page)

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Research on the Classification Model of Similarity Malware using Fuzzy Hash
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Abstract· Keywords

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In the past about 10 different kinds of malicious code were found in one day on the average. However, the number of malicious codes that are found has rapidly increased reachingover 55,000 during the last 10 year. A large number of malicious codes, however, are not new kinds of malicious codes but most of them are new variants of the existing malicious codes as same functions are newly added into the existing malicious codes, or the existing malicious codes are modified to evade anti-virus detection. To deal with a lot of malicious codes including new malicious codes and variants of the existing malicious codes, we need to compare the malicious codes in the past and the similarity and classify the new malicious codes and the variants of the existing malicious codes. A former calculation method of the similarity on the existing malicious codes compare external factors of IPs, URLs, API, Strings, etc or source code levels. The former calculation method of the similarity takes time due to the number of malicious codes and comparable factors on the increase, and it leads to employing fuzzy hashing to reduce the amount of calculation. The existing fuzzy hashing, however, has some limitations, and it causes come problems to the former calculation of the similarity. Therefore, this research paper has suggested a new comparison method for malicious codes to improve performance of the calculation of the similarity using fuzzy hashing and also a classification method employing the new comparison method.
#Fuzzy Hash #Malware #Similarity

Contents

요약
ABSTRACT
I. 서론
II. 배경지식 및 관련연구
III. 악성코드 비교인자 추출 알고리즘 제안
IV. 악성코드 유사도 비교 인자 평가
V. 유사 악성코드 분류모델 제안
VI. 유사 악성코드 분류모델 평가
VII. 결론
참고문헌

References (27)

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1. [인터넷자원] - AV-TEST / / AV-TEST Statistic google schola 2. [보고서] - AhnLab / 2012 / 2011 악성코드 통계 분석 보고서 google schola 3. [인터넷자원] - AhnLab / / V3 최신엔진 업데이트 google schola 4. [인터넷자원] - Hauri / / Virobot 최신엔진 업데이트 목록 google schola 5. [학술대회논문] - Andrew Walenstein / 2006 / TheSoftware Similarity Problem in MalwareAnalysis / In Proceedings DagstuhlSeminar 06301: Duplication, Redundancy,and Similarity in Software,Dagstuhl, Germany : 10 ~ google schola

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