Enhancing the Harris Hawks Optimization Algorithm With Ambush‐Based Operators for Feature Selection in UAV‐Based Intrusion Detection Systems

• Published in: Concurrency and computation Vol. 37; no. 21-22
• Main Authors: Zabihullah Musawi, Sayed, Farshi, Mohammad, Ebrahimi Mood, Sepehr, Souri, Alireza
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 25.09.2025; Wiley Subscription Services, Inc
• Subjects: Ablation; Accuracy; Algorithms; ambush‐based operator; Classification; Datasets; Drone aircraft; Effectiveness; Feature selection; Harris hawks optimization algorithm; intrusion detection system; Intrusion detection systems; Machine learning; Optimization; Rank tests; Sensitivity analysis; UAV networks; Unmanned aerial vehicles
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.70207

ABSTRACT Autonomous vehicles (AVs), including drones, rely on sensors, machine learning algorithms, and large datasets for perception, decision‐making, and control. However, the high dimensionality of these datasets increases computational load and hampers real‐time performance. In Unmanned Aerial Vehicle (UAV) systems, feature selection is critical for reducing complexity and enhancing processing efficiency, thereby enabling faster and more accurate decision‐making. In this study, we enhance the Harris Hawks Optimization (HHO) algorithm by introducing a novel ambush‐based operator to regulate selection pressure, resulting in an improved variant named AMHHO. The effectiveness of AMHHO is validated using IEEE CEC2019 benchmark functions and compared against several well‐known optimization algorithms. To further evaluate its robustness, ablation studies and sensitivity analyses are conducted to identify the most efficient AMHHO variants. Furthermore, a binary version of AMHHO (BAMHHO) is applied to ten high‐dimensional datasets and the UAV‐IDS‐2020 dataset for feature selection and classification tasks. BAMHHO is assessed based on classification accuracy, fitness value, feature selection ratio, and computation time, demonstrating superior performance across multiple datasets and outperforming state‐of‐the‐art methods. To rigorously evaluate the statistical significance of its results, Wilcoxon Signed‐Rank test is applied to compare BAMHHO with other well‐known algorithms, confirming the statistical superiority of BAMHHO. In conclusion, BAMHHO not only achieves effective performance on high‐dimensional datasets but also achieves 100% classification accuracy on the UAV‐IDS‐2020 dataset, all while maintaining an optimal balance between feature reduction and computational efficiency. These findings confirm BAMHHO's effectiveness in handling high‐dimensional data and highlight its potential for application in UAV‐based intrusion detection systems.