Abstract: Embodiments for detecting anomalous consumption of energy are provided. Information associated with energy consumption over a designated period of time is received. A threshold value is received. A classifier based on an Auto-Regressive Moving Average model is applied to the information and a result representing the likelihood of an attack is determined. The result is then analyzed to determine if it attained a threshold value. The information is then classified as indicating an attack. Additionally, embodiments for utilizing machine learning to train a classifier using training data to develop parameters for the auto-regressive moving average model are provided. Further, embodiments for evaluating the effectiveness of the parameters used in the Auto-Regressive Moving Average model to classify data are provided.

Abstract: A first electronic device comprises an information-filtering module configured to identify one or more second electronic devices that supply one or more types of information needed by the first electronic device; an information-gathering module coupled to the information-filtering module configured to collect information from the second electronic devices; and an operation module coupled to the information-gathering module configured to adjust operational behavior of the first electronic device based on the collected information.