| 한글제목(Korean Title) |
A Secure Encryption-Based Malware Detection System |
| 영문제목(English Title) |
A Secure Encryption-Based Malware Detection System |
| 저자(Author) |
Zhaowen Lin
Fei Xiao
Yi Sun
Yan Ma
Cong-Cong Xing
Jun Huang
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| 원문수록처(Citation) |
VOL 12 NO. 04 PP. 1799 ~ 1818 (2018. 04) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Malware detections continue to be a challenging task as attackers may be aware of the rules used in malware detection mechanisms and constantly generate new breeds of malware to evade the current malware detection mechanisms. Consequently, novel and innovated malware detection techniques need to be investigated to deal with this circumstance. In this paper, we propose a new secure malware detection system in which API call fragments are used to recognize potential malware instances, and these API call fragments together with the homomorphic encryption technique are used to construct a privacy-preserving Naive Bayes classifier (PP-NBC). Experimental results demonstrate that the proposed PP-NBC can successfully classify instances of malware with a hit-rate as high as 94.93%.
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| 키워드(Keyword) |
Malware detection
detection mechanism
API call fragments
homomorphic encryption
privacy-preserving Naive Bayes classifier
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| 파일첨부 |
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