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PROACTIVELY PROTECTING SERVICE ENDPOINTS BASED ON DEEP LEARNING OF USER LOCATION AND ACCESS PATTERNS
PROACTIVELY PROTECTING SERVICE ENDPOINTS BASED ON DEEP LEARNING OF USER LOCATION AND ACCESS PATTERNS
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机译:积极保护服务端点基于用户位置和访问模式的深度学习
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摘要
Example implementations relate to proactively protecting service endpoints based on deep learning of user location and access patterns. A machine-learning model is trained to recognize anomalies in access patterns relating to endpoints of a cloud-based service by capturing metadata associated with user accesses. The metadata for a given access includes information regarding a particular user that initiated the given access, a particular device utilized, a particular location associated with the given access and specific workloads associated with the given access. An anomaly relating to an access by a user to a service endpoint is identified by monitoring the access patterns and applying the machine-learning model to metadata associated with the access. Based on a degree of risk to the cloud-based service associated with the identified anomaly, a mitigation action is determined. The cloud-based service is proactively protected by programmatically applying the determined mitigation action.
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