Spaces:
Sleeping
Sleeping
File size: 2,694 Bytes
310bebe ced7467 310bebe ced7467 310bebe ced7467 310bebe ced7467 310bebe ced7467 310bebe ced7467 310bebe ced7467 310bebe ced7467 310bebe ced7467 310bebe ced7467 310bebe ced7467 310bebe aca2d9c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
def detect_ransomware(file):
# Read uploaded CSV file
df = pd.read_csv(file.name)
# Simulated prediction and actual columns for demo
df['predicted'] = df.index % 2
df['actual'] = (df.index + 1) % 2
# Confusion Matrix
cm = pd.crosstab(df['actual'], df['predicted'], rownames=['Actual'], colnames=['Predicted'])
plt.figure(figsize=(4, 4))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
confusion_path = "confusion_matrix.png"
plt.title("Confusion Matrix")
plt.savefig(confusion_path)
plt.close()
# Feature Importance (dummy)
features = ['duration', 'bytes_sent', 'bytes_received']
importance = [0.4, 0.35, 0.25]
plt.figure(figsize=(6, 4))
sns.barplot(x=importance, y=features)
plt.title("Feature Importance")
feature_path = "feature_importance.png"
plt.savefig(feature_path)
plt.close()
# Safe vs Ransomware Pie Chart
safe_count = (df['predicted'] == 0).sum()
ransomware_count = (df['predicted'] == 1).sum()
plt.figure(figsize=(4, 4))
plt.pie(
[safe_count, ransomware_count],
labels=['Safe', 'Ransomware'],
colors=['#4CAF50', '#F44336'],
autopct='%1.1f%%',
startangle=90
)
plt.title("Prediction Distribution: Safe vs Ransomware")
class_dist_path = "class_distribution.png"
plt.savefig(class_dist_path)
plt.close()
# Detection Results
results = {
"Total Records": len(df),
"Safe Connections": int(safe_count),
"Ransomware Connections": int(ransomware_count),
"Ransomware Percentage": f"{(ransomware_count / len(df)) * 100:.2f}%"
}
return confusion_path, feature_path, class_dist_path, results
# Create Gradio interface (Modern syntax)
def create_interface():
iface = gr.Interface(
fn=detect_ransomware,
inputs=gr.File(label="Upload Network Traffic CSV File", file_types=[".csv"]),
outputs=[
gr.Image(label="Confusion Matrix", type="filepath"),
gr.Image(label="Feature Importance", type="filepath"),
gr.Image(label="Prediction Distribution (Pie Chart)", type="filepath"),
gr.JSON(label="Detection Results")
],
title="Ransomware Detection System",
description="""
Upload a network traffic CSV file to detect potential ransomware activity.
The system will analyze the data and provide visualizations of the detection results.
"""
)
return iface
# Launch the app
interface = create_interface()
interface.launch()
interface.launch(share=True) |