阅读说明：本技术 用于实时检测，表征，和补救基于电子邮件的威胁的威胁检测平台 (Threat detection platform for real-time detection, characterization, and remediation of email-based threats ) 是由 桑杰·贾亚库马尔 约书亚·布拉特曼 德米特里·切西克 艾伯哈吉特·巴格里 埃文·雷塞 S· 于 2019-12-18 设计创作，主要内容包括：传统的电子邮件过滤服务不适于识别复杂恶意电子邮件,因此可能会允许复杂恶意电子邮件误入收件箱。本文介绍的是威胁检测平台,旨在采用集成的途径检测安全威胁。例如,在接收到来自个人的表示允许访问企业员工接收到的过去电子邮件的输入后,威胁检测平台可以下载过去电子邮件,以构建理解与内部联系人(例如,其他员工)和/或外部联系人(例如,供应商)的通信常态的机器学习(ML)模型。通过将所述ML模型应用到传入的电子邮件中,威胁检测平台可以有针对性地实时识别安全威胁。(Conventional email filtering services are not suitable for identifying complex malicious emails, and thus may allow the complex malicious emails to be mistakenly entered into an inbox. Presented herein are threat detection platforms that are intended to detect security threats in an integrated approach. For example, upon receiving input from an individual indicating that access to past email received by an enterprise employee is allowed, the threat detection platform may download the past email to build a Machine Learning (ML) model that understands the normality of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., suppliers). By applying the ML model to incoming emails, a threat detection platform may be targeted to identify security threats in real-time.)