Intelligent Machine Learning mechanism for Advanced Persistent Threats (APT) early detection

الملخص

 Advanced Persistent Threats (APTs) are the major risk to the security of the online systems; therefore, its detection is very important. User and Entity Behavior Analytics (UEBA) mechanism detects. APTs by utilizing the machine learning algorithms, APTs are electronic attacks aimed at a particular place, usually governmental or private. Typically, the objective of these cyber-attacks is to steal valuable information from their database. The attack by APTs is a significant issue for the security of information and global networks. APT attacks may be combined with shareware or other software for downloading. Many kinds of APTs do not have difficulty passing the system firewall, their malicious behavior is hidden and they avoid all traditional detection methods with advanced evasion techniques.

Advanced Persistent Threats (APTs) are type of attacks that are very dangerous and they cause a lot of damages in the cyberspace, the main objective of this paper is to design and implement detection and prediction mechanisms of Advanced Persistent Threats (APTs) using cybersecurity and machine learning. Thus, our research paper attempts to find a mechanism to identify the attacks that can be classified as APT attacks