Machine learning methods for cyber security intrusion detection: Datasets and comparative study

Abstract

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.