TY - JOUR
T1 - Consortium blockchain-based malware detection in mobile devices
AU - Gu, Jingjing
AU - Sun, Binglin
AU - Du, Xiaojiang
AU - Wang, Jun
AU - Zhuang, Yi
AU - Wang, Ziwang
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2018/2/12
Y1 - 2018/2/12
N2 - 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.
AB - 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.
KW - Consortium Blockchain
KW - malware detection
KW - multi-feature
UR - http://www.scopus.com/inward/record.url?scp=85042074358&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85042074358&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2018.2805783
DO - 10.1109/ACCESS.2018.2805783
M3 - Article
AN - SCOPUS:85042074358
VL - 6
SP - 12118
EP - 12128
JO - IEEE Access
JF - IEEE Access
ER -