journal article·2025Open access

Enhancing Malware Detection Resilience: A U-Net GAN Denoising Framework for Image-Based Classification

Huiyao Dong, Igor Kotenko

Computers, Materials & Continua· Volume 82· Issue 3

Abstract

The growing complexity of cyber threats requires innovative machine learning techniques, and image-based malware classification opens up new possibilities. Meanwhile, existing research has largely overlooked the impact of noi... | Find, read and cite all the research you need on Tech Science Press
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Research context

OpenAlex topics: Anomaly Detection Techniques and Applications · Advanced Malware Detection Techniques · Network Security and Intrusion Detection

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (40)

Show source-provided references
1. SonicWall. 2024 Mid-year cyber threat report; 2024. [cited 2024 Nov 16]. Available from: https://www.sonicwall.com/2024-Mid-Year-Cyber-Threat-Report.asp.
2. Binary malware image classification using machine learning with local binary pattern
3. MSIC: malware spectrogram image classification10.1109/ACCESS.2020.2999320 ↗
4. Image-based malware analysis for enhanced IoT security in smart cities10.1016/j.iot.2024.101258 ↗
5. A survey of malware detection using deep learning
6. DTMIC: deep transfer learning for malware image classification
7. Obfuscation revealed: leveraging electromagnetic signals for obfuscated malware classification
8. VAE-GAN for Robust IoT malware detection and classification in intelligent urban environments: an image analysis approach
9. Deep multi-task learning for malware image classification
10. Robust IoT malware detection and classification using opcode category features on machine learning10.1109/ACCESS.2023.3247344 ↗
11. A system call-based android malware detection approach with homogeneous & heterogeneous ensemble machine learning10.1016/j.cose.2023.103277 ↗
12. Image-based malware representation approach with EfficientNet convolutional neural networks for effective malware classification
13. An attention mechanism for combination of CNN and VAE for image-based malware classification10.1109/ACCESS.2022.3198072 ↗
14. Hybrid deep learning approach based on LSTM and CNN for malware detection10.1007/s11277-024-11366-y ↗
15. CNN-LSTM hybrid model for enhanced malware analysis and detection10.1016/j.procs.2024.03.239 ↗
16. Convolutional-free malware image classification using self-attention mechanisms
17. Deep learning on image denoising: an overview10.1016/j.neunet.2020.07.025 ↗
18. Multi-scale cross-path concatenation residual network for Poisson denoising10.1049/iet-ipr.2018.5941 ↗
19. Medical image denoising using convolutional neural network: a residual learning approach10.1007/s11227-017-2080-0 ↗
20. Hyperspectral image denoising employing a spatial-spectral deep residual convolutional neural network10.1109/TGRS.2018.2865197 ↗
21. Denoising network of dynamic features for enhanced malware classification
22. BM3D and deep image prior based denoising for the defense against adversarial attacks on malware detection networks
23. Unveiling vulnerabilities in deep learning-based malware detection: differential privacy driven adversarial attacks10.1016/j.cose.2024.104035 ↗
24. Obfuscation algorithm for privacy-preserving deep learning-based medical image analysis10.3390/app12083997 ↗
25. Boncelet · The essential guide to image processing · 2009 · 14310.1016/B978-0-12-374457-9.00007-X ↗
26. A comparative study on approaches to speckle noise reduction in images
27. Speckle noise and the detection of faint companions10.1086/316367 ↗
28. SUNet: swin transformer UNet for image denoising
29. Single image super-resolution based on a modified U-net with mixed gradient loss10.1007/s11760-021-02063-5 ↗
30. Understanding GANs: fundamentals, variants, training challenges, applications, and open problems10.1007/s11042-024-19361-y ↗
31. Loshchilov I, Hutter F. Decoupled weight decay regularization. arXiv:1711.05101. 2019.
32. Ronen R. Microsoft malware classification challenge. arXiv:1802.10135. 2018.
33. Malware images: visualization and automatic classification
34. Densely connected convolutional networks
35. Efficientnet: rethinking model scaling for convolutional neural networks
36. Mobilenetv2: inverted residuals and linear bottlenecks
37. Deep residual learning for image recognition
38. Beyond a gaussian denoiser: residual learning of deep CNN for image denoising10.1109/TIP.2017.2662206 ↗
39. Photo-realistic single image super-resolution using a generative adversarial network
40. Image super-resolution via iterative refinement