TY - GEN
T1 - An image-inspired and CNN-based android malware detection approach
AU - Xiao, Xusheng
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Until 2017, Android smartphones occupied approximately 87% of the smartphone market. The vast market also promotes the development of Android malware. Nowadays, the number of malware targeting Android devices found daily is more than 38,000. With the rapid progress of mobile application programming and anti-reverse-engineering techniques, it is harder to detect all kinds of malware. To address challenges in existing detection techniques, such as data obfuscation and limited code coverage, we propose a detection approach that directly learns features of malware from Dalvik bytecode based on deep learning technique (CNN). The average detection time of our model is0.22 seconds, which is much lower than other existing detection approaches. In the meantime, the overall accuracy of our model achieves over 93%.
AB - Until 2017, Android smartphones occupied approximately 87% of the smartphone market. The vast market also promotes the development of Android malware. Nowadays, the number of malware targeting Android devices found daily is more than 38,000. With the rapid progress of mobile application programming and anti-reverse-engineering techniques, it is harder to detect all kinds of malware. To address challenges in existing detection techniques, such as data obfuscation and limited code coverage, we propose a detection approach that directly learns features of malware from Dalvik bytecode based on deep learning technique (CNN). The average detection time of our model is0.22 seconds, which is much lower than other existing detection approaches. In the meantime, the overall accuracy of our model achieves over 93%.
KW - Android Malware Detection
KW - CNN
KW - Deep learning
UR - http://www.scopus.com/inward/record.url?scp=85078955367&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078955367&partnerID=8YFLogxK
U2 - 10.1109/ASE.2019.00155
DO - 10.1109/ASE.2019.00155
M3 - Conference contribution
AN - SCOPUS:85078955367
T3 - Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
SP - 1259
EP - 1261
BT - Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
Y2 - 10 November 2019 through 15 November 2019
ER -