Android Malware Detection: Looking beyond Dalvik Bytecode
Author:
Affiliation:
1. SnT University of Luxembourg,Luxembourg
Funder
Fonds National de la Recherche (FNR), Luxembourg
University of Luxembourg
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9680270/9679822/09680288.pdf?arnumber=9680288
Reference49 articles.
1. Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network
2. DL-Droid: Deep learning based android malware detection using real devices
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4. DeepVisDroid: android malware detection by hybridizing image-based features with deep learning techniques
5. Android malware detection using convolutional neural networks and data section images
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1. DexBERT: Effective, Task-Agnostic and Fine-Grained Representation Learning of Android Bytecode;IEEE Transactions on Software Engineering;2023-10-01
2. Guided Retraining to Enhance the Detection of Difficult Android Malware;Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis;2023-07-12
3. An effective end-to-end android malware detection method;Expert Systems with Applications;2023-05
4. Android malware detection based on multi-head squeeze-and-excitation residual network;Expert Systems with Applications;2023-02
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