Voting Model Strategies for Reliable Categorical IoT-DDoS Attack Prediction

Abstract

This research focuses on developing reliable categorical IoT-DDoS attack prediction models using ensemble voting strategies. The study explores various machine learning algorithms suitable for categorical data analysis, employing feature engineering techniques to preprocess IoT data. Ensemble learning methodologies, including bagging, boosting, and stacking, are then utilized to build robust prediction models. Evaluation metrics such as precision, recall, F1-score, and AUC-ROC are used to assess model performance, demonstrating the effectiveness of ensemble voting models in reliably predicting IoT-DDoS attacks. Comparative analyses with individual classifiers highlight the advantages of ensemble approaches in terms of predictive accuracy and robustness against data imbalances and noise. This work contributes to advancing IoT security by providing a practical framework for deploying predictive models that aid in early detection and mitigation of DDoS attacks, enhancing overall resilience against cyber threats in IoT ecosystems.