@Article{jcs.2021.016632,
AUTHOR = {Zhangjie Fu, Yongjie Ding, Musaazi Godfrey},
TITLE = {An LSTM-Based Malware Detection Using Transfer Learning},
JOURNAL = {Journal of Cyber Security},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {11--28},
URL = {http://www.techscience.com/JCS/v3n1/42436},
ISSN = {2579-0064},
ABSTRACT = {Mobile malware occupies a considerable proportion of cyberattacks. 
With the update of mobile device operating systems and the development of 
software technology, more and more new malware keep appearing. The 
emergence of new malware makes the identification accuracy of existing 
methods lower and lower. There is an urgent need for more effective malware 
detection models. In this paper, we propose a new approach to mobile malware 
detection that is able to detect newly-emerged malware instances. Firstly, we 
build and train the LSTM-based model on original benign and malware samples 
investigated by both static and dynamic analysis techniques. Then, we build a 
generative adversarial network to generate augmented examples, which can 
emulate the characteristics of newly-emerged malware. At last, we use the
augmented examples to retrain the 4th and 5th layers of the LSTM network and 
the last fully connected layer so that it can discriminate against newly-emerged 
malware. Actual experiments show that our malware detection achieved a 
classification accuracy of 99.94% when tested on augmented samples and 86.5% 
with the samples of newly-emerged malware on real data.},
DOI = {10.32604/jcs.2021.016632}
}