SABV 0.1.1

Signature Agnostic Binary Visualizer based on SAGMAD

SABV (Signature-Agnostic Binary Visualizer)

A PE/binary-to-image converter designed to visualize any binary file.
Optionally integrates a fuzzy-inference system (FIS) inspired by the SAGMAD methodology.

SABV allows:

• Conversion of arbitrary binary files into structured image representations
• Optional fuzzy-inference enhanced visualizations
• Custom color schemes
• Configurable sampling, resolution, and threading

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Installation

pip install SABV

Installation from source

git clone https://github.com/AquaCoder0010/SABV.git
pip install -r requirements.txt

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Example Usage

Basic Visualization (FIS disabled)

from SABV import SABV
import cv2

# visualization without fuzzy inference
sabv = SignatureAgnosticBinaryVisualizer()
img = sabv.process_file("ENTER-FILE-PATH")

cv2.imwrite("IMAGE_PATH.png", img)

Basic Visualization with custom color scheme

from SABV import SABV
import cv2

# note that color_scheme must be a function as well as a function that returns a tuple
def custom_color_scheme(byte):
	if byte > 128:
	   return (128, 128, 128)
	return (255, 255, 255)

sabv = SignatureAgnosticBinaryVisualizer()
sabv.set_color_scheme(custom)
img = sabv.process_file("ENTER-FILE-PATH")
cv2.imwrite("IMAGE_PATH.png", img)

Visualization With Fuzzy Inference System (FIS)

from SABV import SABV
import cv2

sabv_with_fis = SABV(FIS_ENABLED=True, N=3, sample=0.05, FIS_THREADING_ENABLED=True)
img = sabv.process_file("ENTER-FILE-PATH")

cv2.imwrite("IMAGE_PATH.png", img)

Benchmark

Example Images

with FIS

without FIS

Citation

Saridou, B.; Rose, J. R.; Shiaeles, S.; Papadopoulos, B.
SAGMAD—A Signature Agnostic Malware Detection System Based on Binary Visualisation and Fuzzy Sets.
Electronics, 2022, 11, 1044.
https://doi.org/10.3390/electronics11071044