The increase in internet usage brings security problems with it. Malicious software can affect the operation of the systems and disrupt data confidentiality due to the security gaps in the systems. Intrusion Detection Systems (IDS) have been developed to detect and report attacks. In order to develop IDS systems, artificial intelligence-based approaches have been used more frequently. In this study, literature studies using CSE-CIC IDS-2018, UNSW-NB15, ISCX-2012, NSL-KDD and CIDDS-001 data sets, which are widely used to develop IDS systems, are reviewed in detail. In addition, max-min normalization was performed on these data sets and classification was made with support vector machine (SVM), K-Nearest neighbor (KNN), Decision Tree (DT) algorithms, which are among the classical machine learning approaches. As a result, more successful results have been obtained in some of the studies given in the literature. The study is thought to be useful for developing IDS systems on the basis of artificial intelligence with approaches such as machine learning.

互联网使用的增加带来了安全问题。由于系统中的安全漏洞，恶意软件可能会影响系统的运行并破坏数据机密性。入侵检测系统（IDS）已开发用于检测和报告攻击。为了开发IDS系统，已经更加频繁地使用基于人工智能的方法。在这项研究中，详细研究了使用广泛用于开发IDS系统的CSE-CIC IDS-2018，UNSW-NB15，ISCX-2012，NSL-KDD和CIDDS-001数据集的文献研究。此外，对这些数据集进行了最大-最小归一化，并使用了支持向量机（SVM），K最近邻（KNN）和决策树（DT）算法进行分类，它们是经典的机器学习方法。作为结果，在文献中给出的一些研究中获得了更成功的结果。该研究被认为对于基于人工智能（例如机器学习）的IDS系统开发很有用。