Abstract—Malware continues to pose a critical threat to cybersecurity, necessitating intelligent detection systems capable of adapting to evolving attack strategies. This paper introduces an enhanced malware detection framework that integrates bio-inspired feature selection with advanced gradient boosting classifiers to achieve high accuracy and efficiency. Two metaheuristic algorithms—Harris Hawks Optimization (HHO) and the Bat Algorithm (BA)—are independently applied to extract compact and discriminative feature subsets from the ISCX-URL2016 malware dataset. The reduced feature sets are subsequently evaluated using Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LightGBM) classifiers. Performance is rigorously assessed using Accuracy as the primary evaluation metric. Experimental results demonstrate that LightGBM combined with BA achieves the highest performance, reaching an accuracy of 99.52%, precision of 99.48%, recall of 99.48%, and F1-Score of 99.48%. These findings underscore the effectiveness of bio-inspired optimization for feature selection, showing that the proposed framework not only improves predictive performance but also offers a scalable and reliable solution for real-world malware detection.
 
Keywords—malware, machine learning, feature selection, ISCX-URL2016 dataset


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