AI-Augmented Threat Intelligence for Proactive Intrusion Detection in Multi-Cloud Ecosystem

Abstract

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An AI-enhanced threat intelligence tool can detect intrusions before they happen in complex multi-cloud setups. Traditional detection methods don't function as effectively because they can't adapt to changing scenarios or understand cloud activity. The proposed three-step methodology enhances data preparation, identifies multimodal anomalies, and generates semantic insights. It finds key danger patterns utilizing entropy-based filtering, temporal behavior modeling, and unsupervised threat categorization. These patterns are then interpreted using knowledge graphs and adaptive learning. The system's design evolves and employs feedback loops to improve detection logic and reaction protocols. Combining deep learning models with contextual risk information ensures high classification accuracy. Real-time choices with few false positives are possible. The tests demonstrate it's superior at discovering and running stuff. The system finds items 97.8% of the time, delivers real positives 96.9 %, and phony positives 1.5 %. It has the best practical features. The system achieves an AUC-ROC of 98.4 %, a threat response time of 105 ms, and exhibits strong scalability, robustness, and computational efficiency scores. These results demonstrate that the proposed approach is a reliable, adaptable, and extendable cloud security solution. This study establishes a solid foundation for artificial intelligence and contextual semantics to improve cyber resilience. This makes it suitable for massive, multi-cloud company infrastructures.