This article explains how Alibaba tackles anomaly detection for tens of millions of metrics in a 100‑thousand‑machine cluster by comparing vertical time‑series methods with horizontal clustering, choosing DBSCAN for large‑scale monitoring, and detailing the ETL, computation, and visualization pipeline.
The article examines the growing threat of black‑market malicious websites, outlines a five‑part war‑themed framework for comprehensive opponent analysis, detection strategies across traffic, channel, content and relationship dimensions, and advanced detection models—including fingerprint, text, image, graph, and multimodal approaches—while highlighting the supporting operational and intelligence systems.
The article explains what threat intelligence is, classifies it into strategic, tactical, and operational categories, illustrates its critical role in modern cyber‑warfare and e‑commerce defense, and discusses the challenges of timeliness, accuracy, and sharing within the security ecosystem.
