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.
翻译:随着网络攻击次数的增加,网络安全正在演变成任何企业都关心的一个关键问题,人工智能和机器学习(特别是深学习-DL)可以作为关键的网络防御辅助技术,因为它们可以有助于发现威胁,甚至可以向网络分析家提供建议的行动。 有必要在全球范围建立产业、学术界和政府的伙伴关系,以推动通过网络安全方面的AI/ML,并创建高效的网络防御系统。 在本文件中,我们关注对用于网络入侵探测的各种深学习技术的调查,我们引入了网络安全应用DL框架。