A Hybrid HHO-BA Feature Selection Framework for High-Accuracy Malicious URL Detection Using LightGBM

Abstract

Malicious URLs are frequently used as delivery channels for malware and continue to represent a challenge in cybersecurity. The following paper propose URL detection framework based on using a combination of the Harris Hawks Optimizer (HHO) and Bat Algorithm (BA) using a union feature selection strategy. The goal is to build an informative and diverse subset of features by using the features that were chosen using two complementary metaheuristic search methods. LightGBM and Naive Bayes classifiers are used to evaluate the selected features on ISCX-URL2016 dataset. As it has been experimentally found, LightGBM has a higher accuracy of 99.52 % and Naive Bayes has an accuracy of 81.12 % indicating a distinct difference in the capability of modelling structured URL features. The proposed framework is competitive or better accurate when compared to some of the past studies. The results demonstrate that the HHOUBA union approach is successful in minimizing feature redundancy and discriminative information is maintained, which results in higher learning performance and a steady classification performance. The suggested solution is a solid solution to malicious URL detection.