ML Powered Cloud Intrusion Prevention Architecture
Word Count : 6000
Objectives to cover:
Introduction: Overview of machine-learning-powered cloud intrusion prevention systems.
Background & Motivation: Rising cloud attacks demand intelligent automated defense.
Cloud Security Challenges: Traditional signature-based methods fail against evolving threats.
ML in Intrusion Prevention: ML models detect unknown threats through behavior analysis.
System Architecture: Data pipeline, ML engine, threat classifier, and response module.
Data Collection & Features: Logs, network flows, and user behavior patterns for training models.
ML Techniques Used: Supervised, unsupervised, and deep learning for anomaly detection.
Performance & Evaluation: Accuracy, false-positive control, latency, and scalability checks.
Conclusion: ML-enabled intrusion prevention strengthens proactive cloud security and resilience.
Reference: IEEE