سمپوزیوم منطقه ای نوآوری در علم و فناوری

صفحه اصلی / دومین سمپوزیوم منطقه ای نوآوری در علم و فناوری

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

نویسندگان :

Mahdi Karimi¹ Mohammad mahdi Shirmohammadi² Saeedreza Alikhani³

1- دانشگاه علوم پزشکی تهران 2- دانشگاه آزاد اسلامی واحد همدان 3- دانشگاه آزاد اسلامی واحد همدان

کلمات کلیدی :

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

چکیده :

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 99.96 % accuracy. Precision 99.96%, Recall 99.96% and the F1-Score equal to 99.95%, 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