A Survey of Deep Learning Methods for Cyber Security
Обзор методов глубокого обучения для кибербезопасности
2019-04-02
SCID: 54.1/dc56c358
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cyber securitydeep autoencodersdeep learningnetwork intrusionsrecurrent neural networks
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Abstract (AI)
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.
Key Findings
1
Covered threats also include insider threats, network intrusions, false data injection, and malicious botnet domain names.
2
It covers deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and other deep learning methods.
3
The paper provides a broad overview of deep learning across multiple cyberattack categories rather than focusing on a single security problem.
4
The paper surveys deep learning methods applied to cybersecurity, combining literature review with tutorial-style descriptions of major architectures.
5
The survey examines how deep learning techniques address diverse cybersecurity applications, including malware and spam detection.
Research Object
deep learning methods applied to cyber security
Research Subject
the use of deep learning methods for detecting and addressing cyber attacks, including malware, spam, insider threats, network intrusions, false data injection, and malicious botnet domain names
Publication Details
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2019-04-02
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