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

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…