Abstract: A computerized method and system for predicting the probability of a cyberattack to a target entity, includes: collecting a plurality of predictive signals to a target entity for a specific cyberattack type; optionally, imputing a value for missing values of the collected signals; selecting a set of relevant non-redundant signals from the collected signals to create lagged signals; identifying from the lagged signals relevant data chunks to form a custom training set of signals; providing selected ground truth data related to the specific attack type for the target entity; training a forecasting model using the custom training set of signals together with the selected ground truth data related to the specific attack type for the target entity to generate a trained forecasting model; providing a second set of signals of the same type of signals as the custom training set of signals; and generating the probability of the specific attack type of interest against the target entity by inputting the second set of s

Abstract: A computerized method and system for predicting the probability of a cyberattack to a target entity, includes: collecting a plurality of predictive signals to a target entity for a specific cyberattack type; optionally, imputing a value for missing values of the collected signals; selecting a set of relevant non-redundant signals from the collected signals to create lagged signals; identifying from the lagged signals relevant data chunks to form a custom training set of signals; providing selected ground truth data related to the specific attack type for the target entity; training a forecasting model using the custom training set of signals together with the selected ground truth data related to the specific attack type for the target entity to generate a trained forecasting model; providing a second set of signals of the same type of signals as the custom training set of signals; and generating the probability of the specific attack type of interest against the target entity by inputting the second set of s

Abstract: A system and method is disclosed for a remote activity detection process using an analysis of data streams of an entity such as an end user and/or a customer. In an embodiment, the detection process uses the data stream analysis to evaluate an entity's potential involvement in an activity based on individual measures for the entity such as comparison of the entity's data stream to the entity's peers, comparison of the entity's data stream to historical information for the entity, and/or comparison of the entity's data stream to data streams for a known second entity involved in the activity. The detection process may also use other information available which may impact the data points in a data stream, such as premises attributes associated with an entity, demographic attributes for the entity, financial attributes for the entity, and system alerts.

Abstract: A system and method is disclosed for a remote activity detection process using an analysis of data streams of an entity such as an end user and/or a customer. In an embodiment, the detection process uses the data stream analysis to evaluate an entity's potential involvement in an activity based on individual measures for the entity such as comparison of the entity's data stream to the entity's peers, comparison of the entity's data stream to historical information for the entity, and/or comparison of the entity's data stream to data streams for a known second entity involved in the activity. The detection process may also use other information available which may impact the data points in a data stream, such as premises attributes associated with an entity, demographic attributes for the entity, financial attributes for the entity, and system alerts.

Abstract: A system and method is disclosed for a remote activity detection process using an analysis of data streams of an entity such as an end user and/or a customer. In an embodiment, the detection process uses the data stream analysis to evaluate an entity's potential involvement in an activity based on individual measures for the entity such as comparison of the entity's data stream to the entity's peers, comparison of the entity's data stream to historical information for the entity, and/or comparison of the entity's data stream to data streams for a known second entity involved in the activity. The detection process may also use other information available which may impact the data points in a data stream, such as premises attributes associated with an entity, demographic attributes for the entity, financial attributes for the entity, and system alerts.

Abstract: A system and method is disclosed for optimizing a demand response event. Strategizes for demand response events developed according to the present disclosure consider a customer's individual satisfaction ranking in creating a customer-specific demand response participation schedule so that customer dissatisfaction is reduced and a more uniform customer response across the entire demand response event is achieved. Customers participating in a demand response event need not participate in the entire event and can limit their participation to coincide with their individual satisfaction ranking.

Abstract: A system and method is disclosed for a remote activity detection process using an analysis of data streams of an entity such as an end user and/or a customer. In an embodiment, the detection process uses the data stream analysis to evaluate an entity's potential involvement in an activity based on individual measures for the entity such as comparison of the entity's data stream to the entity's peers, comparison of the entity's data stream to historical information for the entity, and/or comparison of the entity's data stream to data streams for a known second entity involved in the activity. The detection process may also use other information available which may impact the data points in a data stream, such as premises attributes associated with an entity, demographic attributes for the entity, financial attributes for the entity, and system alerts.