| import dash |
| from dash import dcc, html |
| import pandas as pd |
| import numpy as np |
| from datetime import datetime, timedelta |
| from sklearn.ensemble import IsolationForest |
| from sklearn.preprocessing import StandardScaler |
| import plotly.graph_objs as go |
|
|
| |
| app = dash.Dash(__name__) |
| server = app.server |
|
|
| |
| def generate_mock_data(n_samples=100): |
| current_time = datetime.now() |
| timestamps = [current_time - timedelta(minutes=i) for i in range(n_samples)] |
| |
| data = pd.DataFrame({ |
| 'timestamp': timestamps, |
| 'network_traffic': np.random.normal(1000, 200, n_samples), |
| 'failed_logins': np.random.poisson(5, n_samples), |
| 'suspicious_ips': np.random.poisson(2, n_samples), |
| 'data_exfiltration': np.random.normal(50, 10, n_samples) |
| }) |
| return data |
|
|
| |
| def detect_anomalies(df): |
| isolation_forest = IsolationForest(contamination=0.1, random_state=42) |
| scaler = StandardScaler() |
| |
| features = ['network_traffic', 'failed_logins', 'suspicious_ips', 'data_exfiltration'] |
| X = df[features] |
| X_scaled = scaler.fit_transform(X) |
| |
| return isolation_forest.fit_predict(X_scaled) == -1 |
|
|
| |
| df = generate_mock_data() |
| anomalies = detect_anomalies(df) |
|
|
| |
| def create_network_traffic_figure(): |
| fig = go.Figure() |
| |
| |
| fig.add_trace(go.Scatter( |
| x=df[~anomalies]['timestamp'], |
| y=df[~anomalies]['network_traffic'], |
| name='Normal Traffic', |
| mode='lines', |
| line=dict(color='blue') |
| )) |
| |
| |
| fig.add_trace(go.Scatter( |
| x=df[anomalies]['timestamp'], |
| y=df[anomalies]['network_traffic'], |
| name='Anomalies', |
| mode='markers', |
| marker=dict(color='red', size=10) |
| )) |
| |
| fig.update_layout( |
| title='Network Traffic with Anomaly Detection', |
| xaxis_title='Time', |
| yaxis_title='Network Traffic (bytes)', |
| template='plotly_white' |
| ) |
| return fig |
|
|
| |
| def generate_metrics(): |
| return { |
| 'Total Anomalies': int(sum(anomalies)), |
| 'Average Network Traffic': f"{float(df['network_traffic'].mean()):.2f}", |
| 'Max Failed Logins': int(df['failed_logins'].max()), |
| } |
|
|
| |
| app.layout = html.Div([ |
| html.H1("AI-Enhanced Cybersecurity Dashboard", |
| style={'textAlign': 'center', 'padding': '20px'}), |
| |
| |
| html.Div([ |
| html.H2("Key Metrics", style={'textAlign': 'center'}), |
| html.Div([ |
| html.Table( |
| [html.Tr([html.Th(k), html.Td(v)]) for k, v in generate_metrics().items()], |
| style={'margin': 'auto', 'border-collapse': 'collapse'} |
| ) |
| ]) |
| ], style={'padding': '20px'}), |
| |
| |
| html.Div([ |
| html.H2("Network Traffic Analysis", style={'textAlign': 'center'}), |
| dcc.Graph(figure=create_network_traffic_figure()) |
| ], style={'padding': '20px'}) |
| ]) |
|
|
| |
| app.index_string = ''' |
| <!DOCTYPE html> |
| <html> |
| <head> |
| {%metas%} |
| <title>Cybersecurity Dashboard</title> |
| {%favicon%} |
| {%css%} |
| <style> |
| body { |
| font-family: Arial, sans-serif; |
| margin: 0; |
| background-color: #f0f2f5; |
| } |
| table { |
| width: 100%; |
| max-width: 600px; |
| } |
| th, td { |
| padding: 12px; |
| text-align: left; |
| border-bottom: 1px solid #ddd; |
| } |
| th { |
| background-color: #f8f9fa; |
| } |
| </style> |
| </head> |
| <body> |
| {%app_entry%} |
| <footer> |
| {%config%} |
| {%scripts%} |
| {%renderer%} |
| </footer> |
| </body> |
| </html> |
| ''' |
|
|
| if __name__ == '__main__': |
| app.run_server(debug=True) |
