Intelligent Firewall for Attack Detection: Integrating Dragonfly and Bat Algorithms with Machine Learning

Abstract

The increasing sophistication of cyber threats necessitates the development of advanced attack detection methods capable of handling high-dimensional network traffic data efficiently. This paper introduces an AI-driven firewall model that leverages the Dragonfly Algorithm (DA) and Bat Algorithm (BA) for optimal feature selection, enhancing attack detection accuracy. The proposed approach utilizes the UNSW-NB15 dataset and employs a union-based feature selection strategy, combining the best-selected features from DA and BA to maximize classification performance. Three classifiers— utilize Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR)—are implemented for attack detection. Experimental results demonstrate that DT achieved 100% accuracy, SVM achieved 99.99% accuracy, while LR achieved 99.94%, confirming the effectiveness of the proposed model. The AI-embedded firewall significantly reduces false positives and enhances detection robustness.