Malware Detection
Using Multi-Channel PE Image Analysis
Advanced malware detection utilizing Multi-Channel PE Image analysis. Our CNN model ensures robust identification against complex packing and padding techniques without disassembly overhead
Drop Files
Supports .exe files up to 200MB
ResNet50
Classification Engine
Powered by Transfer Learning, our optimized ResNet50 model extracts hierarchical and spatial features directly from multi-channel images to detect hidden threats.
Adversarial Robustness
Rigorously evaluated against evasion techniques, ensuring stable detection performance even under spatial Padding and structural Packing (UPX/MPRESS).
GRAYSCALE MAP
Captures the raw byte distribution to recognize global morphological features and structural textures of the binary.
MARKOV TRANSITION MATRIX
Encodes byte transition probabilities to capture the underlying rhythm of program logic, highly effective against metamorphic evasion.
SHANNON ENTROPY
Visualizes localized entropy across sliding windows to highlight regions of high byte randomness, identifying encrypted or packed payloads.
Automated Visual Pipeline
Seamlessly converts raw Windows executables (.exe) into Tri-Channel structural representations—Grayscale, Markov Matrix, and Shannon Entropy—without the need for resource-intensive disassembly.
info: initializing data transformation pipeline...
info: extracting byte sequence from target.exe
status: generating Grayscale map (Channel 1)... done
status: calculating Markov transition matrix (Channel 2)... done
status: mapping Shannon Entropy [window=256, overlap=128] (Channel 3)... done
info: fusing channels into 256x256x3 input tensor...
query: running inference via ResNet50 backbone...
warning: high entropy region detected (potential packing/obfuscation)
status: final confidence score computed.