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Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems

机译:综述:网络安全和入侵检测系统的深度学习方法

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摘要

As the number of cyber-attacks is increasing, cyber-security 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.
机译:随着网络攻击的数量正在增加,网络安全正在不断发展到任何业务的关键问题。人工智能(AI)和机器学习(ML)(特别是深度学习 - DL)可以利用作为网络防御的关键支持技术,因为它们可以促进威胁检测,甚至可以向网络分析师提供建议的行动。为全球范围内的行业,学术界和政府的伙伴关系是必要的,以便推进AI / ML对网络安全并创造有效的网络防御系统。在本文中,我们涉及对网络入侵检测采用各种深度学习技术的调查,我们为网络安全应用引入了DL框架。

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