论文标题
评论：网络安全和入侵检测系统的深度学习方法
Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems
论文作者
论文摘要
随着网络攻击的数量的增加，网络安全正在发展成为任何企业的关键问题。人工智能（AI）和机器学习（ML）（特别是深度学习-DL）可以作为网络防御的关键促进技术，因为它们可以为威胁检测做出贡献，甚至可以为网络分析师提供建议的行动。必须在全球范围内建立工业，学术界和政府的合作伙伴关系，以提高AI/ML对网络安全的采用并创建有效的网络防御系统。在本文中，我们关注对用于网络入侵检测的各种深度学习技术的研究，并引入了用于网络安全应用的DL框架。
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.
