1. A fusion rule-based anomaly detection system, the system comprising: a learning machine agent, which is a physical sensor, a security camera, or another environmental monitoring sensor, for each domain configured to transmit a piece of new information to a data mining engine module operatively connected to the learning machine agent; a normal data profile for each domain executed in an electronic system per domain, wherein the normal data profile of each domain is created by a generic learning procedure and data collection using the learning machine agent per domain; an anomaly detection engine module for each domain operatively connected to the data mining engine module for each domain, wherein the anomaly detection engine is configured to compare the piece of new information against the normal data profile to generate an anomaly score for each domain; a multiple-domain anomaly detection system operatively connected to a plurality of domain-specific anomaly detection systems, wherein each domain-specific anomaly detection system contains its own learning machine agent, its own normal data profile, and its own anomaly detection engine; a weighted or priority fusion rule executed in the multiple-domain anomaly detection system, wherein the weighted or priority fusion rule generates a multiple-domain anomaly score by placing a higher weight or priority on one domain-specific anomaly score over another domain-specific anomaly score, wherein the weighted or priority fusion rule remains isolated from internal data sets and internal requirements of each domain; and a data network operatively connecting the multiple-domain anomaly detection system and the plurality of domain-specific anomaly detection systems for data communication.