Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems
Abstract
As the number of cyber-attacks is increasing, cybersecurity 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.
Cite
@article{arxiv.2012.02891,
title = {Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems},
author = {Mayra Macas and Chunming Wu},
journal= {arXiv preprint arXiv:2012.02891},
year = {2020}
}
Comments
IEEE Latin-American Conference on Communications (LATINCOM) 2020