AI-Driven Cloud Security

Dynamic Policy Optimization with Reinforcement Learning

To overcome limitations of static security policies, we employ Reinforcement Learning (RL), specifically a double Q-learning network. This enables the system to dynamically optimize security policies based on evolving threat patterns.

The RL model continuously refines protection strategies by selecting optimal actions from a rich action space (187 response measures), based on a state space of 232 key security indicators. This adaptive mechanism significantly reduces response time by 76% and decreases false alarms by 35% compared to static approaches.

Integrated Security Protection Framework

Our system features a comprehensive security framework with four integrated modules: data collection, feature extraction, intelligent analysis, and protection execution. This creates a complete security pipeline from real-time monitoring to automated response. Data collection is multi-tiered, gathering network traffic, system logs, and user behavior, preprocessed into 428-dimensional feature vectors.

The intelligent analysis module combines our improved CNN-LSTM deep learning model for threat detection and reinforcement learning for adaptive policy optimization. The protection execution module automates security policy adjustments with an average response time of less than 50ms.

Real-World Performance Validation

Validated in a large-scale real-world business environment, our system processed 85TB of network traffic data, achieving a detection precision of 98.2% and an F1 score of 97.5% for DDoS attacks, and 95.7% precision/95% F1 score for SQL injection. The system demonstrated robust performance and scalability.

Robustness tests confirmed resilience under extreme conditions, including a 4-hour, 850Gbps DDoS attack, maintaining normal operation with resource utilization within safe thresholds.

Performance Comparison with Traditional Systems

Addressing Deployment Challenges & Future Directions

Real-world deployment revealed challenges in integration, performance optimization, and false positive management. We addressed these through containerized deployment, model quantization, edge-based detection, and confidence-based threat scoring.

Future research includes exploring transformer-based models for complex attack behaviors, graph-based security models (GNNs) for attack propagation analysis, and privacy-preserving federated learning for collaborative threat intelligence sharing. We also aim to reduce reliance on labeled data through self-supervised and unsupervised learning for zero-day threat detection.