Cybersecurity Framework for Proactive Detection of Image-Based Malware in Healthcare Networks Using Explainable Deep Ensemble Learning and Edge Analytics
IEEE Transactions on Consumer Electronics, cilt.72, sa.3, ss.7279-7293, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 72 Sayı: 3
- Basım Tarihi: 2026
- Doi Numarası: 10.1109/tce.2026.3665641
- Dergi Adı: IEEE Transactions on Consumer Electronics
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Compendex, INSPEC, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Sayfa Sayıları: ss.7279-7293
- Anahtar Kelimeler: cybersecurity, deep learning, ensemble learning, Healthcare, malware, XAI
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
The fast-paced digitalization of the healthcare sector (Electronic Health Records (EHRs), Internet of Medical Things (IoMT) devices, medical imaging, and telemedicine) has enabled more efficient patient care but has also increased the attack surface of sophisticated threats. These security concerns are emerging in consumer electronics within smart healthcare ecosystems, which rely on image-based analytics and edge-enabled intelligence. In this study, we propose an edge-enabled cybersecurity framework that integrates deep ensemble learning and edge analytics to preemptively identify image-based malware in healthcare networks and provide low-latency inference at the data source. We introduce VGDense-DeepEnsemble (VGGDenseNet-DeepEnsemble), a novel stacked deep learning model that combines VGG16, VGG19, DenseNet121, and DenseNet169 through ensemble learning with a meta-model to enhance detection performance. Explainable AI (XAI) using Grad-CAM was applied to sample images to interpret the decision-making process. We evaluated the proposed model on the Blended Malware Image Dataset, which comprises 31 malware families with a separate test dataset for final assessment. The proposed model attains 96% overall accuracy with macro precision of 97%, macro recall of 96%, and macro F1 score of 96%. These results exceed the performance of the individual backbones (VGG16: 88%, VGG19: 94%, DenseNet121: 93%, DenseNet169: 94%). As revealed through the ROC and precision-recall analysis, separability and robustness are high across all the classes, and confusion matrices reveal significant decreases in misclassifications in challenging families (e.g., Neshta, Sality, Agent, Injector). Grad-CAM explanations indicate that the predictions are informed by discriminative, malware-relevant structures and not background artifacts. These findings show that transparent edge-based deep ensemble models can be an effective, accurate, explainable defense against image-based malware in healthcare networks.