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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