v2.0 · 3 ML models · PE Static Analysis

Detect Malware
Before It Strikes

Military-grade static analysis powered by Random Forest, XGBoost and LightGBM. Upload a binary, get a verdict in seconds.

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ransomware_v2.exeMalicious
RF 94%XGB 97%LGB 99%
RF
XGB
LGB
0
ML Models
RF · XGBoost · LightGBM
0
PE Features
Extracted per binary
0s
Avg Scan Time
Real-time analysis
0%
Detection Rate
On training set

Three Models. One Verdict.

Ensemble of three industry-standard algorithms vote on every binary for maximum accuracy.

🌲
Random ForestRF

Ensemble of 100 decision trees trained on PE header features. Robust against overfitting.

scikit-learn97.1% accuracy
⚡
XGBoostXGB

Gradient boosted trees with regularisation. Excels at tabular PE header data.

xgboost.json97.5% accuracy
🔥
LightGBMLGB

Leaf-wise gradient boost. Fastest inference, high recall on packed malware samples.

lgbm.txt97.9% accuracy

How It Works

01
Upload Binary
Drop any .exe or .dll (x86/x64)
02
Extract 54 Features
pefile parses PE headers, sections, imports, resources
03
3-Model Ensemble
RF · XGBoost · LightGBM vote in parallel
04
Verdict + Report
Score, risk, features, optional VirusTotal check

Ready to scan?

Upload an executable and get a detailed malware report with model scores and VirusTotal integration.

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Diploma Project · Computer Science · 2026