| dc.creator |
Turan, Cemil |
|
| dc.creator |
Zharkynbek, Dinara |
|
| dc.creator |
Amenova, Shakhnaz |
|
| dc.date |
2022-01-01T00:00:00Z |
|
| dc.date.accessioned |
2023-01-09T12:04:16Z |
|
| dc.date.available |
2023-01-09T12:04:16Z |
|
| dc.identifier |
761246e5-951d-42eb-92e1-e0eab6383231 |
|
| dc.identifier |
10.1109/sist54437.2022.9945816 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/761246e5-951d-42eb-92e1-e0eab6383231/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/98008 |
|
| dc.description |
© 2022 IEEE.Mobile devices play a crucial role and have become an essential part of people's life particularly with online applications such as shopping, learning, mailing, etc. Android OS has continued to drive the market for other operating systems since 2012. Traditional Android malware detection methods, such as static, dynamic, hybrid analysis, or the Bayesian model, may show less accuracy to detect recent Android malware. We propose a deep learning method for Android malware detection using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). CNN provides efficient feature extraction from data and the use of additional LSTM layers improves prediction accuracy. According to the test results, CNN-LSTM can provide reliable malware prediction in Android applications. We train and test our approach using the CICMalDroid2020 dataset. The test results show that the CNN-LSTM classifier exceeds with an accuracy of 94%. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Android Malware Classification by CNN-LSTM |
|
| dc.type |
info:eu-repo/semantics/conferenceObject |
|