Real-Time Anomaly Detection and Attack Mitigation for Cloud-Based Content Delivery Paths Using AI

Abstract

Cloud-based content delivery paths, which include client apps, edge PoPs, backbone links & origin services, are being attacked and misconfigured in ways that make them very less reliable, more expensive, and less trustworthy. Because CDN traffic changes in milliseconds and failures quickly spread across more regions, finding and fixing problems must happen in actual time, not on these dashboards that show what happened after the fact. This study suggests an actual time AI-driven pipeline that learns what "normal" delivery behavior is and steps in as soon as it sees a risk. Telemetry is gathered from multiple levels, such as edge access logs, request/response headers, TLS and QUIC handshake metadata, cache hit/miss streams, routing along with their BGP updates, queueing and loss statistics, and origin health indicators. These signals are then combined into time-aligned flow windows. A hybrid model stack has self-supervised sequence encoders for traffic patterns with a lot of different values, graph-based predictors for path & PoP connections, and lightweight online changepoint detectors to find sudden changes. When an anomaly is detected, a policy engine ranks the most likely causes, such as L7 floods, cache poisoning these attempts, bot spikes, route leaks, or regional origin brownouts. It then runs mitigation playbooks, which include adaptive rate limiting, dynamic WAF rules, cache key hardening, smart rerouting, or selective origin shielding. The system is tested by playing back multi-region traces at line rate and adding controlled red-team injections. This tests detection delay, false positive rate & end-to-end user effect under load. The results show that detection is substantially faster (with a median time-to-alert of less than a second), that context-aware fusion greatly reduces noisy alerts, and that mitigation works very well with little collateral throttling. Overall, the method gives CDN operators and cloud service providers a way to maintain their delivery paths stable even when things change quickly. It turns raw information into fast, automatic defense. The same cycle of learning and doing may also be used for multi-cloud ingress, SASE edges, and next-gen content routing, which will make internet-scale delivery safer and more independent.