Improving the accuracy of intrusion detection systems by optimizing random forest algorithm parameters using genetic algorithm

In the field of cyber security, intrusion detection is one of the vital issues that requires high accuracy and efficiency. However, traditional models usually face challenges such as high false alarm rate and inability to identify new attacks. In this article, an improvement model based on genetic algorithms and random forest is presented, which aims to improve the accuracy and efficiency of intrusion detection systems. The proposed method includes the use of the genetic algorithm to optimize the parameters of the random forest model, which is the optimal setting for the intrusion detection model. The results show that the proposed model has been able to diagnose with ۹۹.۹۶% accuracy. Precision ۹۹.۹۶%, Recall ۹۹.۹۶% and the F۱-Score equal to ۹۹.۹۵%, has a much better performance than other existing models. These results show the high power and efficiency of the model in real environments and provide new directions for researchers in this field to further improve intrusion detection systems.

کلیدواژه ها:

Intrusion detection ، genetic algorithm ، random forest algorithm ، information technology ، security ، cyber attacks