RansomTrack hybrid framework detects ransomware at 96% accuracy in under 10 seconds via Radare2 static features, Frida dynamic behaviors, and ensemble ML on a public 165-family dataset.
Improving ransomware detection based on portable executable header using xception convolutional neural network
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CR 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
TL-RL-FusionNet uses frozen transfer learning backbones and a Q-learning agent to adaptively reweight training samples for ransomware detection, reporting 99.1% accuracy on a 1000-sample dataset.
citing papers explorer
-
RansomTrack: A Hybrid Behavioral Analysis Framework for Ransomware Detection
RansomTrack hybrid framework detects ransomware at 96% accuracy in under 10 seconds via Radare2 static features, Frida dynamic behaviors, and ensemble ML on a public 165-family dataset.
-
TL-RL-FusionNet: An Adaptive and Efficient Reinforcement Learning-Driven Transfer Learning Framework for Detecting Evolving Ransomware Threats
TL-RL-FusionNet uses frozen transfer learning backbones and a Q-learning agent to adaptively reweight training samples for ransomware detection, reporting 99.1% accuracy on a 1000-sample dataset.