[AI-009] Build Real-Time AI Anomaly Detection System for Soroban Contract Monitoring

[Nanle-code]

Develop an advanced AI system for detecting anomalous behavior in Soroban contracts:

• Implement unsupervised learning for baseline behavior modeling
• Use deep learning for pattern recognition in transaction streams
• Include real-time monitoring with alerting on suspicious activities
• Support for custom anomaly detection rules and thresholds
• Implement forensic analysis tools for investigating anomalies
• Include integration with incident response workflows
• Support for multi-contract correlation analysis
• Implement adaptive learning to handle evolving contract behavior

Technical Requirements:

• Stream processing architecture for real-time analysis
• Multiple detection algorithms (isolation forest, autoencoders, LSTM)
• Explainable AI for anomaly interpretation
• Integration with existing monitoring systems

Acceptance Criteria:

• Detects >95% of anomalous patterns in test datasets
• False positive rate <5% on normal operations
• Real-time processing with <1 second latency
• Comprehensive test suite with attack scenarios

Difficulty: Expert
Priority: High

[Purity-Euphemia]

Good Day Maintainer, I have experience and would review the issue, implement a clean solution, test it, and submit a pull request.

[Purity-Euphemia]

Good Day Maintainer, I have experience and would review the issue, implement a clean solution, test it, and submit a pull request.