A Survey of Deep Learning Methods for Cyber Security

Abstract

This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets.

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

“…The article also provides a short tutorial on various deep learning architecture. In [637] surveyed the deep learning applications in detection of various attacks including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets. Additionally, the authors discussed the importance of benchmark datasets in Cyber Security.…”

Intrusion Detection using Machine Learning and Deep Learning

“…DL [48] [49] is a subset of ML that imitates the communication of neurons in a brain, because it makes use of deep connections of neurons, It is called as deep learning. In Neural Network architecture the layers are placed one above the other.…”

Artificial Intelligence in the Cyber Domain: Offense and Defense

“…However, some of them just focused on the adopting machine learning methods for cyber problems such as those in [1][2][3][4]. Other research [5,6] just focused on deep learning methods. Additionally, there is a lack of literature dealing with the nefarious use of AI.…”