The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network
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Citations
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831
(2010) · International Conference on Machine Learning · Cited by 13,995
(2011) · Cited by 6,993
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 6,938
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 4,941
Dissecting Android Malware: Characterization and Evolution
Zhou, Y., & Jiang, X. (2012) · 2012 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2012.16 · Cited by 2,133
Cited by
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00815-1 · Cited by 2,678
Deep Learning for Anomaly Detection: A Survey
Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240
Sarker, I. H. (2022) · SN Computer Science · doi:10.1007/s42979-022-01043-x · Cited by 1,208
A Comprehensive Review on Malware Detection Approaches
Aslan, O., & Samet, R. (2020) · IEEE Access · doi:10.1109/access.2019.2963724 · Cited by 649
Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective
Sarker, I. H. (2021) · doi:10.20944/preprints202102.0340.v1 · Cited by 295
Metadata
- Authors
- Wang, W., Zhao, M., & Wang, J.
- Venue
- Journal of Ambient Intelligence and Humanized Computing
- Published
- 2018-04-28
- Volume
- 10
- Issue
- 8
- Pages
- 3035-3043
- ISSN
- 1868-5137 (ISSN-L)
- 1868-5145
- OpenAlex
- W2800912855
- Cited by
- 284
- References
- 53
- FWCI
- 24.68
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network. https://app.cochat.ai/papers/10.1007/s12652-018-0803-6
- The paper
- Wang, W., Zhao, M., & Wang, J. (2019). Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-018-0803-6
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
