Visualizing Malware through AI.

Our proprietary engine transforms complex binary structures into multi-dimensional spatial data, enabling neural networks to "see" threats with 99.9% forensic accuracy.

Phase 01

The Binary Transformation

Converting raw PE disassembly into multi-channel visual input.

PE Disassembly Process

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[SECTION] .text -> channel(R)
[SECTION] .data -> channel(G)
[SECTION] .rdata -> channel(B)
MERGE(R, G, B) -> Tensor(224, 224, 3)

.text

.data

.rdata

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Phase 02

The Neural Blueprint

A multi-layered approach to detecting heuristic anomalies in static structures.

grid_view

Convolutional Layer

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32 Filters • 3x3 arrow_forward_ios

compress

Pooling Layer

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Max Pool • 2x2 arrow_forward_ios

hub

Fully Connected

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Softmax Output arrow_forward_ios

Phase 03

Evasion Resilience

Maintaining forensic integrity against packers and metamorphic code.

THE CHALLENGE

Packer Obfuscation

Traditional signatures fail when file structures are encrypted or packed. Metamorphism shifts code blocks, breaking simple hashing.

OUR SOLUTION

Spatial Persistence

Our CNN looks for spatial relationships that remain constant regardless of encryption. Even when packed, the high-level entropy distribution forms a unique "visual fingerprint."

psychology

Entropy Map

verified_user

Persistence

Ready to secure your pipeline?

Experience the precision of image-based malware detection. Integration takes minutes with our robust developer API and CLI tools.