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所属单位：计算机科学与技术学院/人工智能学院/软件学院

发表刊物：IEEE ACCESS

关键字：Consortium Blockchain malware detection multi-feature.

摘要：To address the problem of detecting malicious codes in malware and extracting the corresponding evidences in mobile devices, we construct a consortium blockchain framework, which is composed of a detecting consortium chain shared by test members and a public chain shared by users. Specifically, in view of different malware families in Android-based system, we perform feature modeling by utilizing statistical analysis method, so as to extract malware family features, including software package feature, permission and application feature, and function call feature. Moreover, for reducing false-positive rate and improving the detecting ability of malware variants, we design a multi-feature detection method of Android-based system for detecting and classifying malware. In addition, we establish a fact-base of distributed Android malicious codes by blockchain technology. The experimental results show that, compared with the previously published algorithms, the new proposed method can achieve higher detection accuracy in limited time with lower false-positive and false-negative rates.

ISSN号：2169-3536

是否译文：否

发表时间：2018-01-01

合写作者：Sun, Binglin,Du, Xiaojiang,汪俊,庄毅,Wang, Ziwang

通讯作者：顾晶晶