Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems


Abstract:

As the number of cyber-attacks is increasing, cyber-security is evolving to a key concern for any business. Artificial Intelligence (AI) and Machine Learning (ML) (in particular Deep Learning - DL) can be leveraged as key enabling technologies for cyber-defense, since they can contribute in threat detection and can even provide recommended actions to cyber analysts. A partnership of industry, academia, and government on a global scale is necessary in order to advance the adoption of AI/ML to cybersecurity and create efficient cyber defense systems. In this paper, we are concerned with the investigation of the various deep learning techniques employed for network intrusion detection and we introduce a DL framework for cybersecurity applications.

Año de publicación:

2020

Keywords:

  • CYBERSECURITY
  • Artificial Intelligence
  • Intrusion Detection Systems
  • Deep Neural Networks
  • Machine learning

Fuente:

scopusscopus

Tipo de documento:

Conference Object

Estado:

Acceso restringido

Áreas de conocimiento:

  • Aprendizaje automático
  • Ciencias de la computación

Áreas temáticas de Dewey:

  • Ciencias de la computación
  • Programación informática, programas, datos, seguridad
  • Funcionamiento de bibliotecas y archivos

Contribuidores: