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# Install required packages if not installed
# pip install streamlit scikit-learn xgboost pandas
import streamlit as st
import pandas as pd
import numpy as np
import joblib
from sklearn.preprocessing import StandardScaler
from xgboost import XGBClassifier
import matplotlib.pyplot as plt
# Load your trained model (save your trained model as 'model.pkl')
model = joblib.load("model.pkl")
# Title
st.title("Network Intrusion Detection System (NIDS)")
# Sidebar for input features
st.sidebar.header("Input Network Features")
# Collect user input
def user_input():
logged_in = st.sidebar.selectbox('logged_in (1=Yes, 0=No)', [0, 1])
count = st.sidebar.slider('count', 0, 500, 100)
serror_rate = st.sidebar.slider('serror_rate', 0.0, 1.0, 0.5)
srv_serror_rate = st.sidebar.slider('srv_serror_rate', 0.0, 1.0, 0.5)
same_srv_rate = st.sidebar.slider('same_srv_rate', 0.0, 1.0, 0.5)
dst_host_count = st.sidebar.slider('dst_host_count', 0, 255, 100)
dst_host_srv_count = st.sidebar.slider('dst_host_srv_count', 0, 255, 100)
dst_host_same_srv_rate = st.sidebar.slider('dst_host_same_srv_rate', 0.0, 1.0, 0.5)
dst_host_serror_rate = st.sidebar.slider('dst_host_serror_rate', 0.0, 1.0, 0.5)
dst_host_srv_serror_rate = st.sidebar.slider('dst_host_srv_serror_rate', 0.0, 1.0, 0.5)
service_http = st.sidebar.selectbox('service_http (1=HTTP, 0=Other)', [0, 1])
flag_S0 = st.sidebar.selectbox('flag_S0 (1=Yes, 0=No)', [0, 1])
flag_SF = st.sidebar.selectbox('flag_SF (1=Yes, 0=No)', [0, 1])
# Add more features as per your final selected 13 features
data = {
'logged_in': logged_in,
'count': count,
'serror_rate': serror_rate,
'srv_serror_rate': srv_serror_rate,
'same_srv_rate': same_srv_rate,
'dst_host_count': dst_host_count,
'dst_host_srv_count': dst_host_srv_count,
'dst_host_same_srv_rate': dst_host_same_srv_rate,
'dst_host_serror_rate': dst_host_serror_rate,
'dst_host_srv_serror_rate': dst_host_srv_serror_rate,
'service_http': service_http,
'flag_S0': flag_S0,
'flag_SF': flag_SF
}
features = pd.DataFrame(data, index=[0])
return features
input_df = user_input()
# Show input
st.subheader('User Input features')
st.write(input_df)
# Make prediction
prediction = model.predict(input_df)
# Output prediction
st.subheader('Prediction')
labels = {0: 'Normal', 1: 'DoS', 2: 'Probe', 3: 'R2L', 4: 'U2R'}
st.write(f"Detected: **{labels[int(prediction[0])]}**")
# Predict probabilities
probabilities = model.predict_proba(input_df)[0]
# Display probabilities table
st.subheader("Class Probabilities:")
labels_list = ['Normal', 'DoS', 'Probe', 'R2L', 'U2R']
prob_df = pd.DataFrame({'Class': labels_list, 'Probability': probabilities})
st.write(prob_df)
# Plot Bar Chart
st.subheader("Class Probability Bar Chart:")
st.bar_chart(prob_df.set_index('Class')) |