AI Cybersecurity Detection System An open-source machine learning-based system for detecting anomalies and potential security threats in network logs. Features

Real-time log processing: Process and analyze log data from various sources Anomaly detection: Detect unusual patterns using isolation forest algorithm Alert generation: Create detailed alerts with severity levels Extensible: Easy to customize for different types of logs and threats

Installation bashCopy# Clone the repository git clone https://github.com/yourusername/ai-cybersecurity-detector.git cd ai-cybersecurity-detector

Install required packages

pip install -r requirements.txt Usage Basic usage example: pythonCopyfrom ai_threat_detector import AIThreatDetector

Initialize the detector

detector = AIThreatDetector()

Train with your log data

detector.train(your_training_logs)

Detect threats in new logs

threats = detector.detect(new_logs)

Generate alerts

alerts = detector.generate_alerts(threats) Demo Run the built-in demo to see the system in action: bashCopypython ai_threat_detector.py How It Works

Log Processing: The system extracts relevant features from raw log data Training: An isolation forest model learns what "normal" patterns look like Detection: New logs are compared against the model to identify anomalies Alert Generation: Suspicious activities are flagged with appropriate severity levels

Contributing Contributions are welcome! Please feel free to submit a Pull Request. License This project is licensed under the MIT License - see the LICENSE file for details.