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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from pyod.models.iforest import IForest | |
| from pyod.models.lof import LOF | |
| def main(): | |
| st.title("AI-Based Network Anomaly Detection (Predictive Maintenance)") | |
| st.markdown( | |
| """ | |
| This application uses AI to detect unusual behavior in a network before it leads to failure. | |
| By leveraging open source models and PyOD, it predicts potential issues, enabling proactive maintenance. | |
| """ | |
| ) | |
| # Sidebar settings for model and parameters | |
| st.sidebar.header("Settings") | |
| model_choice = st.sidebar.selectbox("Select Anomaly Detection Model", ("Isolation Forest", "Local Outlier Factor")) | |
| contamination = st.sidebar.slider("Contamination (Expected anomaly ratio)", 0.0, 0.5, 0.1) | |
| uploaded_file = st.file_uploader("Upload CSV file with network data", type=["csv"]) | |
| if uploaded_file is not None: | |
| data = pd.read_csv(uploaded_file) | |
| st.write("### Data Preview") | |
| st.dataframe(data.head()) | |
| else: | |
| st.info("No file uploaded. Generating synthetic network data for demonstration.") | |
| # Generate synthetic data with features like traffic, latency, and packet_loss | |
| np.random.seed(42) | |
| n_samples = 300 | |
| traffic = np.random.normal(100, 10, n_samples) | |
| latency = np.random.normal(50, 5, n_samples) | |
| packet_loss = np.random.normal(0.5, 0.1, n_samples) | |
| # Introduce anomalies by modifying a subset of data points | |
| anomaly_indices = np.random.choice(n_samples, size=20, replace=False) | |
| traffic[anomaly_indices] *= 1.5 | |
| latency[anomaly_indices] *= 2 | |
| packet_loss[anomaly_indices] *= 5 | |
| data = pd.DataFrame({ | |
| "traffic": traffic, | |
| "latency": latency, | |
| "packet_loss": packet_loss | |
| }) | |
| st.write("### Synthetic Data") | |
| st.dataframe(data.head()) | |
| # Use only numeric features for anomaly detection | |
| features = data.select_dtypes(include=[np.number]).columns.tolist() | |
| if not features: | |
| st.error("No numeric columns found in the data for anomaly detection.") | |
| return | |
| X = data[features].values | |
| # Initialize the selected model from PyOD | |
| if model_choice == "Isolation Forest": | |
| model = IForest(contamination=contamination) | |
| elif model_choice == "Local Outlier Factor": | |
| model = LOF(contamination=contamination) | |
| # Fit the model and predict anomalies (0: normal, 1: anomaly) | |
| model.fit(X) | |
| predictions = model.labels_ | |
| data["anomaly"] = predictions | |
| st.subheader("Anomaly Detection Results") | |
| st.write(data.head()) | |
| n_anomalies = np.sum(predictions) | |
| st.write(f"Detected **{n_anomalies}** anomalies out of **{len(data)}** data points.") | |
| # Visualization (if at least 2 numeric features are available) | |
| if len(features) >= 2: | |
| st.subheader("Visualization") | |
| fig, ax = plt.subplots() | |
| # Plot using the first two numeric features | |
| x_feature = features[0] | |
| y_feature = features[1] | |
| normal_data = data[data["anomaly"] == 0] | |
| anomaly_data = data[data["anomaly"] == 1] | |
| ax.scatter(normal_data[x_feature], normal_data[y_feature], label="Normal", color="blue", alpha=0.5) | |
| ax.scatter(anomaly_data[x_feature], anomaly_data[y_feature], label="Anomaly", color="red", marker="x") | |
| ax.set_xlabel(x_feature) | |
| ax.set_ylabel(y_feature) | |
| ax.legend() | |
| st.pyplot(fig) | |
| if __name__ == "__main__": | |
| main() | |