Abstract: Techniques for calculating risk scores of entity assignments are discussed herein. The system generates a probability matrix using a collaborative filtering technique such as singular value decomposition. The probability matrix is populated with probability values for each entity representing a probability that, based on the various relationships or associations of that entity with other entities, the entity has been granted an assignment. Risk values are used to provide a weighting value to assignments, separating relatively higher risk assignments from relatively lower risk assignments. The system thereafter calculates a risk score for one or more of the entities using the information in the assignment matrix, the probability matrix, and the risk values. The system can flag or identity one or more entities whose risk scores do not meet various criteria.

Abstract: Techniques for calculating risk scores of entity assignments are discussed herein. The system generates a probability matrix using a collaborative filtering technique such as singular value decomposition. The probability matrix is populated with probability values for each entity representing a probability that, based on the various relationships or associations of that entity with other entities, the entity has been granted an assignment. Risk values are used to provide a weighting value to assignments, separating relatively higher risk assignments from relatively lower risk assignments. The system thereafter calculates a risk score for one or more of the entities using the information in the assignment matrix, the probability matrix, and the risk values. The system can flag or identity one or more entities whose risk scores do not meet various criteria.

Abstract: Techniques for calculating risk scores of entity assignments are discussed herein. The system generates a probability matrix using a collaborative filtering technique such as singular value decomposition. The probability matrix is populated with probability values for each entity representing a probability that, based on the various relationships or associations of that entity with other entities, the entity has been granted an assignment. Risk values are used to provide a weighting value to assignments, separating relatively higher risk assignments from relatively lower risk assignments. The system thereafter calculates a risk score for one or more of the entities using the information in the assignment matrix, the probability matrix, and the risk values. The system can flag or identity one or more entities whose risk scores do not meet various criteria.

Abstract: Techniques for calculating risk scores of entity assignments are discussed herein. The system generates a probability matrix using a collaborative filtering technique such as singular value decomposition. The probability matrix is populated with probability values for each entity representing a probability that, based on the various relationships or associations of that entity with other entities, the entity has been granted an assignment. Risk values are used to provide a weighting value to assignments, separating relatively higher risk assignments from relatively lower risk assignments. The system thereafter calculates a risk score for one or more of the entities using the information in the assignment matrix, the probability matrix, and the risk values. The system can flag or identity one or more entities whose risk scores do not meet various criteria.

Abstract: Methods and systems for multi-cloud breach detection using ensemble classification and deep anomaly detection are disclosed. According to an implementation, a security appliance may receive logged event data. The security appliance may determine using a supervised machine learning (ML) model, a first anomaly score representing a first context. The security appliance may further determine using a semi-supervised machine learning (ML) model, a second anomaly score representing the second context, and using an unsupervised ML model, one or more third anomaly scores representing one or more third contexts. The security appliance may aggregate the first anomaly score, the second anomaly score and the one or more third anomaly scores using a classification module to produce a final anomaly score and a final context. The security appliance may determine that an anomaly exists and a type of attack based on the final anomaly score and the final context.