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[cited 2024 Nov 16]. Available from: https:\/\/www.sonicwall.com\/2024-Mid-Year-Cyber-Threat-Report.asp."},{"key":"ref2","series-title":"2017 IEEE International Conference on Big Data (Big Data)","first-page":"4664","article-title":"Binary malware image classification using machine learning with local binary pattern","author":"Luo","year":"2017"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"102007","DOI":"10.1109\/ACCESS.2020.2999320","article-title":"MSIC: malware spectrogram image classification","volume":"8","author":"Azab","year":"2020","journal-title":"IEEE Access"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"101258","DOI":"10.1016\/j.iot.2024.101258","article-title":"Image-based malware analysis for enhanced IoT security in smart cities","volume":"27","author":"Dong","year":"2024","journal-title":"Int Things"},{"key":"ref5","first-page":"100546","article-title":"A survey of malware detection using deep learning","volume":"16","author":"Bensaoud","year":"2024","journal-title":"Mach Learn Appl"},{"key":"ref6","first-page":"103063","article-title":"DTMIC: deep transfer learning for malware image classification","volume":"64","author":"Kumar","year":"2022","journal-title":"J Inf Secur Appl"},{"key":"ref7","series-title":"Proceedings of the 37th Annual Computer Security Applications Conference","first-page":"706","article-title":"Obfuscation revealed: leveraging electromagnetic signals for obfuscated malware classification","author":"Pham","year":"2021"},{"key":"ref8","series-title":"International Conference on Risks and Security of Internet and Systems","first-page":"200","article-title":"VAE-GAN for Robust IoT malware detection and classification in intelligent urban environments: an image analysis approach","author":"Dong","year":"2023"},{"key":"ref9","first-page":"103057","article-title":"Deep multi-task learning for malware image classification","volume":"64","author":"Bensaoud","year":"2022","journal-title":"J Inf Secur Appl"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"18855","DOI":"10.1109\/ACCESS.2023.3247344","article-title":"Robust IoT malware detection and classification using opcode category features on machine learning","volume":"11","author":"Lee","year":"2023","journal-title":"IEEE Access"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"103277","DOI":"10.1016\/j.cose.2023.103277","article-title":"A system call-based android malware detection approach with homogeneous & heterogeneous ensemble machine learning","volume":"130","author":"Bhat","year":"2023","journal-title":"Comput Secur"},{"key":"ref12","first-page":"103306","article-title":"Image-based malware representation approach with EfficientNet convolutional neural networks for effective malware classification","volume":"69","author":"Chaganti","year":"2022","journal-title":"J Inf Secur Appl"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"85127","DOI":"10.1109\/ACCESS.2022.3198072","article-title":"An attention mechanism for combination of CNN and VAE for image-based malware classification","volume":"10","author":"Dao","year":"2022","journal-title":"IEEE Access"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1007\/s11277-024-11366-y","article-title":"Hybrid deep learning approach based on LSTM and CNN for malware detection","volume":"136","author":"Thakur","year":"2024","journal-title":"Wirel Pers Commun"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/j.procs.2024.03.239","article-title":"CNN-LSTM hybrid model for enhanced malware analysis and detection","volume":"233","author":"Karat","year":"2024","journal-title":"Procedia Comput Sci"},{"key":"ref16","first-page":"1869","article-title":"Convolutional-free malware image classification using self-attention mechanisms","volume":"23","author":"Dong","year":"2024","journal-title":"Inform Autom"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.neunet.2020.07.025","article-title":"Deep learning on image denoising: an overview","volume":"131","author":"Tian","year":"2020","journal-title":"Neural Netw"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1049\/iet-ipr.2018.5941","article-title":"Multi-scale cross-path concatenation residual network for Poisson denoising","volume":"13","author":"Su","year":"2019","journal-title":"IET Image Process"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1007\/s11227-017-2080-0","article-title":"Medical image denoising using convolutional neural network: a residual learning approach","volume":"75","author":"Jifara","year":"2019","journal-title":"J Supercomput"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/TGRS.2018.2865197","article-title":"Hyperspectral image denoising employing a spatial-spectral deep residual convolutional neural network","volume":"57","author":"Yuan","year":"2019","journal-title":"IEEE Trans Geoscience Remote Sens"},{"key":"ref21","series-title":"2023 IEEE International Performance, Computing, and Communications Conference (IPCCC)","first-page":"32","article-title":"Denoising network of dynamic features for enhanced malware classification","author":"Li","year":"2023"},{"key":"ref22","first-page":"163","article-title":"BM3D and deep image prior based denoising for the defense against adversarial attacks on malware detection networks","volume":"10","author":"Kumi","year":"2021","journal-title":"Int J Adv Smart Converg"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"104035","DOI":"10.1016\/j.cose.2024.104035","article-title":"Unveiling vulnerabilities in deep learning-based malware detection: differential privacy driven adversarial attacks","volume":"146","author":"Taheri","year":"2024","journal-title":"Comput Secur"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"3997","DOI":"10.3390\/app12083997","article-title":"Obfuscation algorithm for privacy-preserving deep learning-based medical image analysis","volume":"12","author":"Popescu","year":"2022","journal-title":"Appl Sci"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/B978-0-12-374457-9.00007-X","author":"Boncelet","year":"2009","journal-title":"The essential guide to image processing"},{"key":"ref26","series-title":"2015 International Conference on Computational Intelligence and Networks","first-page":"148","article-title":"A comparative study on approaches to speckle noise reduction in images","author":"Maity","year":"2015"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1086\/316367","article-title":"Speckle noise and the detection of faint companions","volume":"111","author":"Racine","year":"1999","journal-title":"Publ Astron Soc Pac"},{"key":"ref28","series-title":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","first-page":"2333","article-title":"SUNet: swin transformer UNet for image denoising","author":"Fan","year":"2022"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1007\/s11760-021-02063-5","article-title":"Single image super-resolution based on a modified U-net with mixed gradient loss","volume":"16","author":"Lu","year":"2022","journal-title":"Signal, Image Video Process"},{"key":"ref30","doi-asserted-by":"crossref","DOI":"10.1007\/s11042-024-19361-y","article-title":"Understanding GANs: fundamentals, variants, training challenges, applications, and open problems","author":"Ahmad","year":"2024","journal-title":"Multimed Tools Appl"},{"key":"ref31","unstructured":"Loshchilov I, Hutter F. Decoupled weight decay regularization. arXiv:1711.05101. 2019."},{"key":"ref32","unstructured":"Ronen R. Microsoft malware classification challenge. arXiv:1802.10135. 2018."},{"key":"ref33","series-title":"Proceedings of the 8th International Symposium on Visualization for Cyber Security","first-page":"1","article-title":"Malware images: visualization and automatic classification","author":"Nataraj","year":"2011"},{"key":"ref34","series-title":"Proceedings of the IEEE Conference on Comput Vis Pattern Recognit","first-page":"4700","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"ref35","series-title":"International Conference on Machine Learning","first-page":"6105","article-title":"Efficientnet: rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019"},{"key":"ref36","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"4510","article-title":"Mobilenetv2: inverted residuals and linear bottlenecks","author":"Sandler","year":"2018"},{"key":"ref37","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a gaussian denoiser: residual learning of deep CNN for image denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans Image Process"},{"key":"ref39","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"4681","article-title":"Photo-realistic single image super-resolution using a generative adversarial network","author":"Ledig","year":"2017"},{"key":"ref40","first-page":"4713","article-title":"Image super-resolution via iterative refinement","volume":"45","author":"Saharia","year":"2022","journal-title":"IEEE Trans Pattern Analysis Mach Intell"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-82-3\/TSP_CMC_62439\/TSP_CMC_62439.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T06:33:05Z","timestamp":1763101985000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v82n3\/59947"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":40,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.062439","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}