Now a day’s advancement in technology increases the use of automation, mobility, smart devices, and application over the Internet that can create serious problems for protection and the privacy of digital data and raised the global security issues. Therefore, the necessity of intelligent systems or techniques can prevent and protect the data over the network. Cyberattack is the most prominent problem of cybersecurity and now a challenging area of research for scientists and researchers. These attacks may destroy data, system, and resources and sometimes may damage the whole network. Previously numerous traditional techniques were used for the detection and mitigation of cyberattack, but the techniques are not efficient for new attacks. Today’s machine learning and metaheuristic techniques are popularly applied in different areas to achieve efficient computation and fast processing of complex data of the network. This paper is discussing the improvements and enhancement of security models, frameworks for the detection of cyberattacks, and prevention by using different machine learning and optimization techniques in the domain of cybersecurity. This paper is focused on the literature of different metaheuristic algorithms for optimal feature selection and machine learning techniques for the classification of attacks, and some of the prominent algorithms such as GA, evolutionary, PSO, machine learning, and others are discussed in detail. This study provides descriptions and tutorials that can be referred from various literature citations, references, or latest research papers. The techniques discussed are efficiently applied with high performance for detection, mitigation, and identification of cyberattacks and provide a security mechanism over the network. Hence, this survey presents the description of various existing intelligent techniques, attack datasets, different observations, and comparative studies in detail.

中文翻译：

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智能技术在网络攻击检测中的应用比较分析

现在，一天的技术进步增加了自动化、移动性、智能设备和互联网应用程序的使用，这可能会给数字数据的保护和隐私带来严重问题，并引发全球安全问题。因此，智能系统或技术的必要性可以防止和保护网络上的数据。网络攻击是网络安全中最突出的问题，现在对科学家和研究人员来说是一个具有挑战性的研究领域。这些攻击可能会破坏数据、系统和资源，有时可能会损坏整个网络。此前，许多传统技术被用于检测和缓解网络攻击，但这些技术对于新的攻击并不有效。当今的机器学习和元启发式技术广泛应用于不同领域，以实现对网络复杂数据的高效计算和快速处理。本文讨论了通过在网络安全领域使用不​​同的机器学习和优化技术来改进和增强安全模型、网络攻击检测框架和预防。本文重点介绍了用于优化特征选择的不同元启发式算法和用于攻击分类的机器学习技术的文献，并详细讨论了一些突出的算法，如 GA、进化、PSO、机器学习等。本研究提供了可以从各种文献引用、参考资料或最新研究论文中引用的描述和教程。所讨论的技术以高性能有效应用于网络攻击的检测、缓解和识别，并通过网络提供安全机制。因此，本次调查详细介绍了各种现有的智能技术、攻击数据集、不同的观察和比较研究。