USB-IDS-1 dataset feature reduction with genetic algorithm

Abstract

Technology and online opportunities brought by technology are increasing day by day. Many transactions, from banking to shopping, can be done online. However, the abuse of technology is also increasing at the same rate. Therefore, it is very important to ensure the security of the network for data protection. The application of artificial intelligence-based approaches has also become popular in the field of information security. When the data collected for intrusion detection is examined, it is seen that there are many features. In this study, the features in the USB-IDS-1 dataset were reduced by genetic algorithm and its success was examined with various classifiers. Among the selected methods, there are decision trees, random forest, k-NN, Naive Bayes and artificial neural networks. Accuracy, sensitivity, precision and F1-score were used as metrics. According to the results obtained, it was seen that the genetic algorithm was quite successful in the Hulk and Slowloris data set, it was partially effective in the Slowhttptest data, but was not successful in the TCP set. However, the performance of the algorithms was poor as a result of using all features in Slowhttptest and TCP data.