A Machine Learning-Based Firewall Model for Effective Attack Detection Using Dragonfly and Bat Algorithms

Abstract

This work proposes a new machine learning (ML)-based firewall model for attack detection in modern networks. In this regard, the proposed ML-based firewall model is integrated with an advanced feature selection method to optimize the significant features that will improve the accuracy of detection by using the Dragonfly Algorithm (DA) and Bat Algorithm (BA). Logistic Regression (LR) and Gradient Boosting Trees (GBT) were used for the classification in this model. The model was tested and validated on the UNSW-NB15 dataset for the experimentation process because this dataset represents a comprehensive modern network activity. The GBT classifier achieved an accuracy of 100%, demonstrating its great capability in handling selected features and finding the attacks. The LR also attained a very high of accuracy of 99.84%. These outcomes highlight the efficiency of the proposed model in detecting attacks with minimal false positives. The integration of DA and BA for feature selection and the use of robust classifiers make the proposed ML-based firewall a promising solution for safeguarding modern networks.