C. Vijayaraj, Vishwith Reddy, L. Manohar Goud, K. Vishnu Vardhan 02 7(1) Botnet attacks particularly those employing Domain Generation Algorithms DGA contribute to over 40 of commandandcontrol traffic in cyber incidents with global losses from botnetdriven attacks exceeding 2 billion annually Traditional detection systems have shown an average false positive rate of 2030 significantly impacting response efficiency and operational trust Studies indicate that explainability in machine learning models improves analyst decision validation by up to 35 in realworld cybersecurity applications Existing manual detection techniques suffer from limited scalability high analyst dependency and lack of consistent interpretability making them ineffective in realtime or highvolume network environments Additionally traditional methods often fail to detect obfuscated or rapidly evolving DGA patterns due to their static rulebased nature and inability to provide insights into the rationale behind decisions To overcome these challenges this work proposes an efficient Ensemble Explainable AI XAIbased collaborative defense mechanism for botnet detection with a focus on enhancing interpretability accuracy and operational trust The system utilizes the Botnet DGA dataset and employs a structured pipeline comprising data preprocessing cleaning normalization transformation SHAPbased feature attribution for transparency and classification using the XGBoost ensemble model The model is trained to differentiate between normal and botnet network traffic with realtime testing supported by the same interpretability mechanisms The final stage includes a robust performance evaluation using metrics such as accuracy precision and recall Furthermore collaborative threat intelligence sharing is integrated to refine detection capabilities across organizational boundaries ensuring adaptive protection against emerging botnet threats while fostering a trusted and explainable cybersecurity ecosystem Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026