Semantic Scholar Open Access 2018 928 sitasi

Machine Learning and Deep Learning Methods for Cybersecurity

Yang Xin Lingshuang Kong Zhi Liu Yuling Chen Yanmiao Li +4 lainnya

Lihat Sumber DOI

Abstrak

With the development of the Internet, cyber-attacks are changing rapidly and the cyber security situation is not optimistic. This survey report describes key literature surveys on machine learning (ML) and deep learning (DL) methods for network analysis of intrusion detection and provides a brief tutorial description of each ML/DL method. Papers representing each method were indexed, read, and summarized based on their temporal or thermal correlations. Because data are so important in ML/DL methods, we describe some of the commonly used network datasets used in ML/DL, discuss the challenges of using ML/DL for cybersecurity and provide suggestions for research directions.

Topik & Kata Kunci

Computer Science

Penulis (9)

Yang Xin

Lingshuang Kong

Zhi Liu

Yuling Chen

Yanmiao Li

Hongliang Zhu

Mingcheng Gao

Haixia Hou

Chunhua Wang

Format Sitasi

APA MLA BibTeX

Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H. et al. (2018). Machine Learning and Deep Learning Methods for Cybersecurity. https://doi.org/10.1109/ACCESS.2018.2836950

Akses Cepat

Lihat di Sumber doi.org/10.1109/ACCESS.2018.2836950

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 928×
Sumber Database: Semantic Scholar
DOI: 10.1109/ACCESS.2018.2836950
Akses: Open Access ✓