Review: Deep Learning Methods for Cybersecurity and Intrusion Detection
Systems
- URL: http://arxiv.org/abs/2012.02891v1
- Date: Fri, 4 Dec 2020 23:09:35 GMT
- Title: Review: Deep Learning Methods for Cybersecurity and Intrusion Detection
Systems
- Authors: Mayra Macas, Chunming Wu
- Abstract summary: Artificial Intelligence (AI) and Machine Learning (ML) can be leveraged as key enabling technologies for cyber-defense.
In this paper, we are concerned with the investigation of the various deep learning techniques employed for network intrusion detection.
- Score: 6.459380657702644
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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