Recognition of Threats in Hybrid Wireless Sensor Networks by Integrating Harris Hawks with Gradient Boosting Algorithm

Due to the increasing sophistication and complexity of cyber-attacks, particularly in Hybrid Wireless Sensor Networks (HWSNs), digital community infrastructures face significant security challenges.The Gradient Boosting Machine (GBM) is known for its strong predictive capabilities in hazard identification, while Harris Hawks Optimization (HHO), inspired by hawk hunting behavior, enhances the efficient exploration and exploitation of the search space.The proposed method involves pre-processing the data to ensure cleanliness and consistency, followed by the application of HHO and GBM for threat detection, using the NSL-KDD, WSN-DS, and CIDDS-001 datasets.HHO's iterative optimization process accelerates convergence toward optimal solutions, while GBM builds a robust and accurate threat detection model.This advanced approach provides network administrators and security experts with a powerful tool to protect HWSNs from malicious activities, offering real-world applicability.With high detection accuracy and efficiency, it is well-suited to address evolving threats and ensure the availability and integrity of critical infrastructure in modern network environments.Using Python for implementation, the model achieved exceptional results, with 99.6% accuracy on NSL-KDD, 99.1% on CIDDS-001, and 98.9% on WSN-DS when HHO and GBM were combined for threat recognition in HWSNs.