An advanced persistent threat (APT) can be defined as a targeted and very sophisticated cyber attack. IT administrators need tools that allow for the early detection of these attacks. Several approaches have been proposed to provide solutions to this problem based on the attack life cycle. Recently, machine learning techniques have been implemented in these approaches to improve the problem of detection. This paper aims to propose a new approach to APT detection, using machine learning techniques, and is based on the life cycle of an APT attack. The proposed model is organised into two passive stages and three active stages to adapt the mitigation techniques based on machine learning.

中文翻译：

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关于高级持续威胁的新提议：调查

高级持续威胁 (APT) 可以定义为有针对性且非常复杂的网络攻击。IT 管理员需要能够及早发现这些攻击的工具。已经提出了几种方法来基于攻击生命周期为这个问题提供解决方案。最近，在这些方法中实施了机器学习技术以改善检测问题。本文旨在提出一种新的 APT 检测方法，使用机器学习技术，并基于 APT 攻击的生命周期。所提出的模型分为两个被动阶段和三个主动阶段，以适应基于机器学习的缓解技术。