With the increase in the usage of the Internet, a large amount of information is exchanged between different communicating devices. The data should be communicated securely between the communicating devices and therefore, network security is one of the dominant research areas for the current network scenario. Intrusion detection systems (IDSs) are therefore widely used along with other security mechanisms such as firewall and access control. Many research ideas have been proposed pertaining to the IDS using machine learning (ML) techniques, deep learning (DL) techniques, and swarm and evolutionary algorithms (SWEVO). These methods have been tested on the datasets such as DARPA, KDD CUP 99, and NSL-KDD using network features to classify attack types. This paper surveys the intrusion detection problem by considering algorithms from areas such as ML, DL, and SWEVO. The survey is a representative research work carried out in the field of IDS from the year 2008 to 2020. The paper focuses on the methods that have incorporated feature selection in their models for performance evaluation. The paper also discusses the different datasets of IDS and a detailed description of recent dataset CIC IDS-2017. The paper presents applications of IDS with challenges and potential future research directions. The study presented, can serve as a pedestal for research communities and novice researchers in the field of network security for understanding and developing efficient IDS models.

随着互联网使用的增加，大量的信息在不同的通信设备之间交换。数据应该在通信设备之间安全地通信，因此，网络安全是当前网络场景的主要研究领域之一。因此，入侵检测系统 (IDS) 与其他安全机制（例如防火墙和访问控制）一起被广泛使用。已经提出了许多关于使用机器学习 (ML) 技术、深度学习 (DL) 技术以及群和进化算法 (SWEVO) 的 IDS 的研究思路。这些方法已经在 DARPA、KDD CUP 99 和 NSL-KDD 等数据集上进行了测试，使用网络特征对攻击类型进行分类。本文通过考虑来自 ML 等领域的算法来调查入侵检测问题，DL 和 SWEVO。该调查是2008年至2020年在IDS领域开展的具有代表性的研究工作。本文重点介绍了将特征选择纳入其模型进行性能评估的方法。论文还讨论了 IDS 的不同数据集以及最近数据集 CIC IDS-2017 的详细描述。本文介绍了 IDS 的应用挑战和潜在的未来研究方向。提出的研究可以作为网络安全领域的研究社区和新手研究人员理解和开发有效 IDS 模型的基础。本文重点介绍将特征选择纳入其模型以进行性能评估的方法。论文还讨论了 IDS 的不同数据集以及最近数据集 CIC IDS-2017 的详细描述。本文介绍了 IDS 的应用挑战和潜在的未来研究方向。提出的研究可以作为网络安全领域的研究社区和新手研究人员理解和开发有效 IDS 模型的基础。本文重点介绍将特征选择纳入其模型以进行性能评估的方法。论文还讨论了 IDS 的不同数据集以及最近数据集 CIC IDS-2017 的详细描述。本文介绍了 IDS 的应用挑战和潜在的未来研究方向。提出的研究可以作为网络安全领域的研究社区和新手研究人员理解和开发有效 IDS 模型的基础。