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:

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