Open Access
| Issue |
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
|
|
|---|---|---|
| Article Number | 01068 | |
| Number of page(s) | 12 | |
| DOI | https://doi.org/10.1051/epjconf/202532801068 | |
| Published online | 18 June 2025 | |
- Y. Lecun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436-444 (2015). https://doi.org/10.1038/nature14539 [CrossRef] [Google Scholar]
- K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. Int. Conf. Learn. Represent. (2015). https://arxiv.org/abs/1409.1556 [Google Scholar]
- K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2016, 770-778. https://doi.org/10.1109/CVPR.2016.90 [Google Scholar]
- J. Saxe, K. Berlin, Deep neural network-based malware detection using two-dimensional binary program features. Proc. 10th Int. Conf. Malicious Unwanted Softw. 2015, 11-20. https://doi.org/10.1109/MALWARE.2015.7413680 [Google Scholar]
- S. Hou, A. Saas, L. Chen, Y. Ye, Deep4maldroid: A deep learning framework for Android malware detection based on Linux kernel system call graphs. Proc. ACM Workshop Artif. Intell. Secur. 2016, 1-8. https://doi.org/10.1145/2996758.2996764 [Google Scholar]
- Q. Gu, J. Zhou, Z. Li, A transfer learning framework for network traffic classification. ACM Trans. Sens. Netw. 13(4), 1-22 (2017). https://doi.org/10.1145/3086451 [Google Scholar]
- P. Shijo, A. Salim, Integrated static and dynamic analysis for malware detection. Procedia Comput. Sci. 46, 804-811 (2015). https://doi.org/10.1016/j.procs.2015.02.152 [CrossRef] [Google Scholar]
- J. Zhang, X. Shen, Transfer learning in deep learning: A survey. Front. Comput. Sci. 14(2), 172-188 (2020). https://doi.org/10.1007/s11704-019-8206-z [Google Scholar]
- O. Russakovsky, J. Deng, H. Su, et al., ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211-252 (2015). https://doi.org/10.1007/s11263-015-0816-y [Google Scholar]
- B. Yuan, H. Wu, A comprehensive survey on transfer learning for malware detection. J. Inf. Secur. Appl. 54, 102586 (2020). https://doi.org/10.1016/j.jisa.2020.102586 [Google Scholar]
- W. Wang, M. Zhu, X. Zeng, X. Ye, Y. Sheng, Malware traffic classification using convolutional neural network for representation learning. IEEE Access 7, 63473-63485 (2019). https://doi.org/10.1109/ACCESS.2019.2916500 [Google Scholar]
- L. Nataraj, S. Karthikeyan, G. Jacob, B.S. Manjunath, Malware images: visualization and automatic classification. ACM Trans. Manag. Inf. Syst. 2(1), 1-20 (2019). https://doi.org/10.1145/2089094.2089098 [Google Scholar]
- S. Rathore, J.H. Park, Deep learning-based malware detection in industrial IoT environments. Comput. Commun. 154, 1-8 (2020). https://doi.org/10.1016/j.comcom.2020.02.012 [CrossRef] [Google Scholar]
- S. Iqbal, R. Kousar, S.U. Amin, Hybrid malware detection using machine learning techniques. J. King Saud Univ. Comput. Inf. Sci. (2021). https://doi.org/10.1016/j.jksuci.2021.01.005 [Google Scholar]
- S. Chen, R.A. Bridges, Automated malware classification using deep transfer learning. Digit. Threats Res. Pract. 2(1), 1-20 (2021). https://doi.org/10.1145/3448043 [CrossRef] [Google Scholar]
- D. Liu, Y. Xie, Y. Zhang, A deep learning-based method for detection of malicious JavaScript code. IEEE Access 8, 181234-181245 (2020). https://doi.org/10.1109/ACCESS.2020.3028845 [Google Scholar]
- M. Nasr, A.N. Mahmood, M. Portmann, Transfer learning for zero-day malware detection. J. Netw. Comput. Appl. 177, 102947 (2021). https://doi.org/10.1016/i.inca.2020.102947 [Google Scholar]
- A. Salehi, A. Sami, A. Movaghar, Adversarial deep learning for robust malware detection. Expert Syst. Appl. 174, 114667 (2021). https://doi.org/10.1016/j.eswa.2021.114667 [Google Scholar]
- Y. Bai, Z. Ding, Y. Guo, Malware classification using transfer learning with limited training data. Comput. Secur. 113, 102562 (2022). https://doi.org/10.1016/j.cose.2021.102562 [Google Scholar]
- D. Sharifrazi, R. Alizadehsani, J.H. Joloudari, et al., CNN-ELM hybrid model for COVID-19 detection using chest X-ray images. Neural Comput. Appl. 34(1), 1-10 (2022). https://doi.org/10.1007/s00521-021-05639-8 [Google Scholar]
- M. Basak, M.-M. Han, CyberSentinel: A Transparent Defense Framework for Malware Detection in High-Stakes Operational Environments. Sensors 24(11), 3406 (2024). https://doi.org/10.3390/s24113406 [CrossRef] [PubMed] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.