A bio inspired hybrid optimization framework for efficient real time malware detection

Abstract The exponential growth of malware attacks, particularly those exploiting malicious URLs, poses a significant threat to cybersecurity in real-time digital environments. To address the challenges of high-dimensional feature spaces and the need for fast, accurate detection, this study proposes a hybrid bio-inspired optimization framework that combines Harris Hawks Optimization (HHO) and the Bat Algorithm (BA) for effective feature selection. The framework evaluates two strategies—union (HHO∪BA) and intersection (HHO∩BA)—to balance detection performance and computational efficiency. After feature selection, classifiers including XGBoost and Extra Trees are fine-tuned using Grid Search to ensure optimal performance. Experiments are conducted on the ISCX-URL2016 dataset, which includes a comprehensive set of benign and malware-labeled URLs. Results show that the HHO∪BA approach achieves the highest detection accuracy (up to 99.52%) and robust classification metrics, making it ideal for high-security applications where accuracy is critical. In contrast, the HHO∩BA method offers significantly faster training and inference times, making it more suitable for real-time or resource-constrained environments. These findings highlight the trade-off between accuracy and speed and provide a flexible framework that can be adapted to various cybersecurity deployment scenarios.