The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
Image-Based malware classification using ensemble of CNN architectures (IMCEC)
Open accessAbstract
Abstract Both researchers and malware authors have demonstrated that malware scanners are unfortunately limited and are easily evaded by simple obfuscation techniques. This paper proposes a novel ensemble convolutional neural networks (CNNs) based architecture for effective detection of both packed and unpacked malware. We have named this method Image-based Malware Classification using Ensemble of CNNs (IMCEC). Our main assumption is that based on their deeper architectures different CNNs provide different semantic representations of the image; therefore, a set of CNN architectures makes it possible to extract features with higher qualities than traditional methods. Experimental results show that IMCEC is particularly suitable for malware detection. It can achieve a high detection accuracy with low false alarm rates using malware raw-input. Result demonstrates more than 99% accuracy for unpacked malware and over 98% accuracy for packed malware. IMCEC is flexible, practical and efficient as it takes only 1.18 s on average to identify a new malware sample.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
Mohamed Shakeel, P., Baskar, S., Sarma Dhulipala, V. R., Mishra, S., & Jaber, M. M. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1045-z · Cited by 195
RetractedDeep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Williams, C. K. I. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s269 · Cited by 4,281
Li, Z., & Hoiem, D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2773081 · Cited by 4,202
Universal Language Model Fine-tuning for Text Classification
Howard, J., & Ruder, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1031 · Cited by 4,100
Cohen, W. W. (1995) · Machine Learning Proceedings 1995 · doi:10.1016/b978-1-55860-377-6.50023-2 · Cited by 3,787
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277
Cited by
Khraisat, A., & Alazab, A. (2021) · Cybersecurity · doi:10.1186/s42400-021-00077-7 · Cited by 508
Layer-Based Communication-Efficient Federated Learning with Privacy Preservation
LIAN, Z., WANG, W., HUANG, H., & SU, C. (2022) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021bcp0006 · Cited by 33
Uche James, U., Idika, C. N., & Anebi Enyejo, L. (2023) · International Journal of Scientific Research in Computer Science, Engineering and Information Technology · doi:10.32628/cseit23564522 · Cited by 29
Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network
Wang, N., Ren, Z., Zhang, Z., & Fu, J. (2022) · Applied Sciences · doi:10.3390/app12104987 · Cited by 13
Metadata
- Authors
- Vasan, D., Alazab, M., Wassan, S., Safaei, B., & Zheng, Q.
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 92
- Pages
- 101748
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W3008497156
- Cited by
- 401
- References
- 71
- FWCI
- 32.95
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Image-Based malware classification using ensemble of CNN architectures (IMCEC). https://app.cochat.ai/papers/10.1016/j.cose.2020.101748
- The paper
- Vasan, D., Alazab, M., Wassan, S., Safaei, B., & Zheng, Q. (2020). Image-Based malware classification using ensemble of CNN architectures (IMCEC). Computers & Security. https://doi.org/10.1016/j.cose.2020.101748
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.
