Anomalies are early whispers of malfunction, intrusion, or even discovery. Anomaly Detection - Methods, Complexities, and Applications offers a concise, practice-oriented guide to turning those whispers into actionable insights. Blending attention‑enhanced supervision, ensemble learning, and variational auto‑encoders with sector‑specific experience from smart manufacturing, renewable‑rich power grids, secure e‑commerce platforms, and astrophysical observatories, the volume gives readers a panoramic view of data pre‑processing, imbalance handling, explainability, edge deployment, and real‑time analytics. By uniting algorithmic rigor with implementation detail, the book equips engineers, researchers, and graduate students to design resilient monitoring systems, reduce operational risk, and unlock new knowledge from large, noisy datasets.