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FortifAI - Real-Time Log Monitoring & Anomaly Detection Pipeline

Python 3.11 Apache Kafka PostgreSQL Streamlit Docker Compose

🎯 Overview

FortifAI is a production-ready, real-time log monitoring and anomaly detection system that leverages Apache Kafka for high-throughput data streaming and Isolation Forest machine learning for intelligent anomaly detection.

Key Features

  • πŸš€ Real-time Log Streaming: Apache Kafka-powered continuous data pipeline
  • πŸ€– ML-Based Anomaly Detection: Isolation Forest algorithm for detecting:
    • Response time spikes
    • Rare/unusual log messages
    • Timing anomalies
    • Frequency patterns
    • Error rate spikes
  • πŸ“Š Interactive Dashboard: Streamlit-based visualization with auto-refresh
  • πŸ”” Smart Alerting: Severity-based alert system
  • πŸ“ˆ Trend Analysis: Historical anomaly and log volume trends
  • 🐳 Containerized: Full Docker Compose deployment

πŸ—οΈ Architecture

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”     β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”     β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚   Log Producer  │────▢│  Apache Kafka   │────▢│ Anomaly Detectorβ”‚
β”‚   (Synthetic)   β”‚     β”‚   (Streaming)   β”‚     β”‚ (Isolation Forest)
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜     β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜     β””β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                                                          β”‚
                                                          β–Ό
                        β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”     β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
                        β”‚    Streamlit    │◀────│   PostgreSQL    β”‚
                        β”‚   Dashboard     β”‚     β”‚   (Storage)     β”‚
                        β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜     β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

πŸ› οΈ Technology Stack

Component Technology
Message Broker Apache Kafka 7.5
Database PostgreSQL 15
ML Engine Scikit-learn (Isolation Forest)
Dashboard Streamlit 1.29
Visualization Plotly
Data Processing Pandas, NumPy
Containerization Docker, Docker Compose
Language Python 3.11

πŸ“ Project Structure

FortifAI/
β”œβ”€β”€ docker-compose.yml        # Container orchestration
β”œβ”€β”€ .env.example              # Environment variables template
β”œβ”€β”€ README.md                 # This file
β”‚
β”œβ”€β”€ producer/                 # Log generation service
β”‚   β”œβ”€β”€ log_producer.py       # Kafka producer with synthetic logs
β”‚   β”œβ”€β”€ requirements.txt
β”‚   └── Dockerfile
β”‚
β”œβ”€β”€ consumer/                 # Anomaly detection service
β”‚   β”œβ”€β”€ anomaly_detector.py   # Kafka consumer + ML pipeline
β”‚   β”œβ”€β”€ requirements.txt
β”‚   └── Dockerfile
β”‚
β”œβ”€β”€ dashboard/                # Visualization service
β”‚   β”œβ”€β”€ app.py                # Streamlit dashboard
β”‚   β”œβ”€β”€ requirements.txt
β”‚   └── Dockerfile
β”‚
β”œβ”€β”€ models/                   # Machine learning models
β”‚   └── isolation_forest_model.py
β”‚
β”œβ”€β”€ database/                 # Database layer
β”‚   β”œβ”€β”€ init.sql              # PostgreSQL schema
β”‚   └── db_handler.py         # Database operations
β”‚
β”œβ”€β”€ config/                   # Configuration
β”‚   └── config.py
β”‚
└── utils/                    # Utilities
    └── helpers.py

πŸš€ Quick Start

Prerequisites

  • Docker & Docker Compose
  • 4GB+ RAM available for containers
  • Ports 8080, 8501, 5432, 9092, 29092, 2181 available

1. Clone and Configure

cd FortifAI
cp .env.example .env

2. Start All Services

# Build and start all containers
docker-compose up --build -d

# View logs
docker-compose logs -f

3. Access Services

Service URL Description
Dashboard http://localhost:8501 Streamlit monitoring dashboard
Kafka UI http://localhost:8080 Kafka topic browser
PostgreSQL localhost:5432 Database (user: fortifai)

4. Stop Services

docker-compose down

# Remove volumes (clears data)
docker-compose down -v

πŸ“Š Dashboard Features

Key Metrics

  • Total logs processed
  • Anomalies detected
  • Error rate percentage
  • Average response time
  • Active services count
  • Unacknowledged alerts

Visualizations

  • Log Volume Chart: Stacked area chart by log level over time
  • Anomaly Trends: Bar chart showing anomaly types by hour
  • Service Health: Pie chart distribution and error rate by service
  • Response Time Distribution: Histogram with P50/P95/P99 percentiles

Real-time Features

  • Auto-refresh (configurable 5-120 seconds)
  • Live anomaly table with severity badges
  • Active alerts section
  • Filter by time range, service, log level, anomaly type

πŸ€– Anomaly Detection

Isolation Forest Algorithm

The system uses Isolation Forest, an unsupervised learning algorithm ideal for anomaly detection because:

  • No labeled data required: Learns normal patterns automatically
  • Handles high-dimensional data: Multiple features from logs
  • Online learning support: Adapts to changing patterns
  • Efficient: O(n log n) complexity

Feature Extraction

Features extracted from each log entry:

Feature Description
response_time_ms Request latency
log_level Severity (DEBUG to CRITICAL)
status_code HTTP response code
is_success/error flags Status code categories
method HTTP method (GET, POST, etc.)
payload_size Request/response size
service Encoded service identifier
endpoint Encoded API endpoint
hour_sin/cos Cyclical hour encoding
day_sin/cos Cyclical day encoding

Anomaly Types Detected

  1. TIMING_ANOMALY: Unusually high response times
  2. PATTERN_ANOMALY: Unusual error patterns
  3. FREQUENCY_ANOMALY: Statistical outliers
  4. RARE_MESSAGE: Unusual status codes or messages
  5. SPIKE: Sudden bursts of anomalies

βš™οΈ Configuration

Environment Variables

# Database
POSTGRES_HOST=localhost
POSTGRES_PORT=5432
POSTGRES_USER=fortifai
POSTGRES_PASSWORD=fortifai_secret_2024
POSTGRES_DB=log_monitoring

# Kafka
KAFKA_BOOTSTRAP_SERVERS=localhost:29092
KAFKA_TOPIC=logs
KAFKA_GROUP_ID=anomaly-detector-group

# ML Model
ANOMALY_CONTAMINATION=0.1  # Expected anomaly ratio
BATCH_SIZE=100             # Processing batch size

# Producer
LOG_INTERVAL=0.5           # Seconds between logs

Model Parameters

In models/isolation_forest_model.py:

IsolationForest(
    contamination=0.1,      # Expected anomaly rate
    n_estimators=100,       # Number of trees
    max_samples='auto',     # Samples per tree
    random_state=42         # Reproducibility
)

πŸ“ˆ Performance

Benchmarks (8-core CPU, 16GB RAM)

Metric Value
Log throughput ~1000 logs/second
Detection latency <100ms per batch
Dashboard refresh ~2 seconds
Memory usage ~2GB total

Scaling Recommendations

  • Higher throughput: Add Kafka partitions, increase consumer replicas
  • More services: Increase PostgreSQL connection pool
  • Better ML accuracy: Tune contamination, increase training data

πŸ”§ Development

Local Development Setup

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r consumer/requirements.txt
pip install -r dashboard/requirements.txt
pip install -r producer/requirements.txt

# Start infrastructure only
docker-compose up -d zookeeper kafka postgres kafka-ui

# Run services locally
python producer/log_producer.py &
python consumer/anomaly_detector.py &
streamlit run dashboard/app.py

Running Tests

# Unit tests
pytest tests/ -v

# Integration tests
pytest tests/integration/ -v --docker

πŸ“ API Reference

Database Schema

logs table:

  • id, timestamp, service_name, log_level, message
  • response_time_ms, status_code, endpoint, method
  • metadata (JSONB)

anomalies table:

  • id, log_id, timestamp, anomaly_type
  • anomaly_score, severity, description
  • feature_values (JSONB)

alerts table:

  • id, timestamp, alert_type, severity
  • title, message, service_name
  • is_acknowledged, acknowledged_at

🀝 Contributing

  1. Fork the repository
  2. Create feature branch (git checkout -b feature/amazing-feature)
  3. Commit changes (git commit -m 'Add amazing feature')
  4. Push to branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

πŸ“„ License

This project is licensed under the MIT License - see the LICENSE file for details.

πŸ™ Acknowledgments

  • Apache Kafka for robust message streaming
  • Scikit-learn for the Isolation Forest implementation
  • Streamlit for the intuitive dashboard framework
  • Plotly for beautiful visualizations

Built with ❀️ for intelligent log monitoring

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