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PROGRAMMATIC DISCOVERY, RETRIEVAL, AND ANALYSIS OF COMMUNICATIONS TO IDENTIFY ABNORMAL COMMUNICATION ACTIVITY

机译:编程发现,检索和通信分析以识别异常通信活动

摘要

Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.
机译:传统的电子邮件过滤服务不适合识别复杂的恶意电子邮件,因此可能允许错误地允许复杂的恶意电子邮件。 此处介绍的是威胁检测平台,旨在采取综合方法来检测安全威胁。 例如,在接收到指示个人批准的输入后,通过企业员工接收的个人批准,威胁检测平台可以下载过去的电子邮件来构建机器学习(ML)模型,了解与内部联系人的通信规范 (例如,其他员工)和/或外部触点(例如,供应商)。 通过将ML模型应用于来电,威胁检测平台可以以目标方式实时识别安全威胁。

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