Lightweight models achieve competitive botnet detection on CTU-13 with Random Forest at ROC-AUC 0.97 and PR-AUC 0.54 while training 90% faster than CNN baselines.
Botnet detection on CTU-13 using lightweight machine learning models
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Real-ESRGAN delivers the best perceptual sharpness and edge detail at higher magnifications while SwinIR better preserves structural diagnostic features and SRCNN runs efficiently at low magnifications across multiple medical imaging domains.
citing papers explorer
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Botnet Detection on CTU-13 Using Lightweight Machine Learning Models
Lightweight models achieve competitive botnet detection on CTU-13 with Random Forest at ROC-AUC 0.97 and PR-AUC 0.54 while training 90% faster than CNN baselines.
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MedSR-Vision: Deep Learning Framework for Multi-Domain Medical Image Super-Resolution
Real-ESRGAN delivers the best perceptual sharpness and edge detail at higher magnifications while SwinIR better preserves structural diagnostic features and SRCNN runs efficiently at low magnifications across multiple medical imaging domains.