Enhancing intrusion detection: combining LogitBoost algorithms and random forest

Network data security is an issue that affects individuals, businesses, and governments worldwide. As attacks become more common and attackers' tactics evolve, it is important to implement advanced network security solutions such as an intrusion detection system (IDS) to detect unwanted and unexpected network activity. To that end, this article proposes a comprehensive strategy for improving detection performance through classification approaches. If only one classifier is utilised, the final decision may be erroneous, as incorrect classifier output may occur. The ensemble classification method combines multiple classifiers and produces better results than a single classifier. To improve classification accuracy, the proposed model incorporates random forest and LogitBoost. The proposed model has an accuracy of 95.89%, 99.91%, and 98.54% on the UNSW-NB15, CICIDS2017, and CICIDS2018 datasets, respectively, and outperforms other existing models in terms of accuracy, detection rate, and false alarm rate.