4.6 Article

A New Proposal on the Advanced Persistent Threat: A Survey

期刊

APPLIED SCIENCES-BASEL
卷 10, 期 11, 页码 -

出版社

MDPI
DOI: 10.3390/app10113874

关键词

advanced persistent threats; machine learning; malware detection; cybersecurity

资金

  1. Ministerio de Ciencia, Innovacion y Universidades (MCIU, Spain) [TIN2017-84844-C2-2-R]
  2. Agenda Estatal de Investigacion (AEI, Spain) [TIN2017-84844-C2-2-R]
  3. Fondo Europeo de Desarrollo Regional (FEDER, UE) [TIN2017-84844-C2-2-R]
  4. Consejeria de Educacion (Junta de Castilla y Leon, Spain) [SA054G18]
  5. IFARHU-SENACYT scholarship program (Panama)

向作者/读者索取更多资源

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.

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