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import gradio as gr
import joblib
import numpy as np
# Load your saved model
model = joblib.load('cybersecurity_model.joblib')
# Replace this list with your actual feature names in the order your model expects
FEATURE_NAMES = [
'A_frequency', 'NS_frequency', 'CNAME_frequency', 'SOA_frequency', 'NULL_frequency',
'PTR_frequency', 'HINFO_frequency', 'MX_frequency', 'TXT_frequency', 'AAAA_frequency',
'SRV_frequency', 'OPT_frequency', 'rr_type', 'rr_count', 'rr_name_entropy',
'rr_name_length', 'distinct_ns', 'distinct_ip', 'unique_country', 'unique_asn',
'distinct_domains', 'reverse_dns', 'a_records', 'unique_ttl', 'ttl_mean',
'ttl_variance', 'timestamp', 'FQDN_count', 'subdomain_length', 'upper',
'lower', 'numeric', 'entropy'
]
def predict(*inputs):
# Convert inputs to numpy array, reshape for prediction
data = np.array(inputs).reshape(1, -1)
# Predict class label
prediction = model.predict(data)[0]
# Optionally, return class probabilities:
# proba = model.predict_proba(data)[0]
# return prediction, proba
return prediction
# Create Gradio inputs dynamically based on feature names
inputs = [gr.Number(label=feat) for feat in FEATURE_NAMES]
# Gradio Interface setup
iface = gr.Interface(
fn=predict,
inputs=inputs,
outputs="text",
title="Cybersecurity Attack Detection",
description="Input the feature values and predict whether the network activity is benign or an attack."
)
if __name__ == "__main__":
iface.launch()
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