malware-detector-tensorflow

🦠 Malware Detector : TensorFlow Neural Network

Pre-trained models for the Malware Detector project a deep learning binary classifier that detects malware based on Linux process memory features, served via a Gradio web interface.

🧠 Model Architecture

A 6-layer fully connected neural network built with TensorFlow/Keras:

Input (33 features)
    → Dense(50, relu)
    → Dense(50, relu)
    → Dense(50, relu)
    → Dense(50, relu)
    → Dense(50, relu)
    → Dense(50, relu)
    → Dense(2, softmax)   ← Benign / Malware

Training config:

• Batch size: 100
• Max epochs: 10
• Early stopping: patience = 2
• Train/test split: 80/20 (stratified)
• Feature scaling: StandardScaler

📦 Files

🗂️ Dataset

Source: Malware Detection Using Deep Learning Dataset

33 Linux process memory features including: millisecond, state, prio, vm_pgoff, task_size, map_count, total_vm, utime, stime, nvcsw, min_flt and more.

⚠️ Known limitation: Dataset has class imbalance toward Malware samples. Future improvement: apply SMOTE or class weighting during training.

🚀 Usage

import tensorflow as tf
import joblib
import pandas as pd

# Load model and scaler
model = tf.keras.models.load_model('malware_model.h5')
scaler = joblib.load('scaler.pkl')

# Load and preprocess your data
df = pd.read_csv('your_data.csv')
df = df.drop(['hash', 'classification'], axis=1, errors='ignore')
X_scaled = scaler.transform(df)

# Predict
predictions = model.predict(X_scaled)
results = ['Benign' if r[0] > r[1] else 'Malware' for r in predictions]
print(results)

🔗 Full Project

For the complete source code and Gradio interface: 👉 github.com/Sakni-Tasnim/malware-detector-tensorflow

👤 Author

Sakni Tasnim Telecommunications & Computer Engineering Student
🔗 GitHub • LinkedIn