Current - Issue
Year 2026 · Volume 6 · Issue 5
Original Article
Malware Classification Under class Imbalance Using Deep Learning Technique with Image Modality
Patil N S1
Adithi M Vithalkar2
Likitha B U3
Shreya G A4
Siri G Y5
1 2 3 4 5 Students, Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India.
Published Online: September-October 2026
Pages: 131-135
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20260605015
References
1. L. Nataraj, S. Karthikeyan, G. Jacob, and B. S. Manjunath, “Malware Images: Visualization and Automatic Classification,” in Proc.
International Symposium on Visualization for Cyber Security, 2011.
2. T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal Loss for Dense Object Detection,” IEEE Transactions on Pattern Analysis
and Machine Intelligence, 2017.
3. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proc. IEEE Conference on Computer Vision
and Pattern Recognition, 2016.
4. M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in Proc. International Conference on
Machine Learning, 2019.
5. C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On Calibration of Modern Neural Networks,” in Proc. International Conference on
Machine Learning, 2017.
6. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” Journal of
Artificial Intelligence Research, vol. 16, pp. 321–357, 2002.
7. Shorten and T. M. Khoshgoftaar, “A Survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, article 60,
2019.
8. Chaganti, V. Ravi, and T. D. Pham, “EfficientNet-Based Malware Family Classification Using Byte-Level Image Representation,” 2022.
9. Marastoni, F. Giacobazzi, and M. Dalla Preda, “Obfuscation-Based Augmentation and Transfer Learning for Malware Classification,”
2021.
10. The project report, “Visualization-Based Malware Classification under Class Imbalance using EfficientNet-B0,” Department of
Information Science and Engineering, BIET, Davanagere.
International Symposium on Visualization for Cyber Security, 2011.
2. T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal Loss for Dense Object Detection,” IEEE Transactions on Pattern Analysis
and Machine Intelligence, 2017.
3. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proc. IEEE Conference on Computer Vision
and Pattern Recognition, 2016.
4. M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in Proc. International Conference on
Machine Learning, 2019.
5. C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On Calibration of Modern Neural Networks,” in Proc. International Conference on
Machine Learning, 2017.
6. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” Journal of
Artificial Intelligence Research, vol. 16, pp. 321–357, 2002.
7. Shorten and T. M. Khoshgoftaar, “A Survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, article 60,
2019.
8. Chaganti, V. Ravi, and T. D. Pham, “EfficientNet-Based Malware Family Classification Using Byte-Level Image Representation,” 2022.
9. Marastoni, F. Giacobazzi, and M. Dalla Preda, “Obfuscation-Based Augmentation and Transfer Learning for Malware Classification,”
2021.
10. The project report, “Visualization-Based Malware Classification under Class Imbalance using EfficientNet-B0,” Department of
Information Science and Engineering, BIET, Davanagere.
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