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