Hyperparameter optimization of XGBoost and hybrid CnnSVM for cyber threat detection using modified Harris hawks algorithm

Abstract

The escalating complexity of cyber threats in smart microgrids necessitates advanced detection frameworks to counter sophisticated attacks. Existing methods often underutilize optimization techniques like Harris hawks optimization (HHO) and struggle with class imbalance in cybersecurity datasets. This study proposes a novel framework integrating HHO with extreme gradient boosting (XGBoost) and a hybrid convolutional neural network with support vector machine (Cnn-SVM) to enhance cyber threat detection. Using the distributed denial of service (DDoS) botnet attack and KDD CUP99 datasets, the proposed models leverage HHO for hyperparameter optimization, achieving accuracies of 99.97% and 99.99%, respectively, alongside improved area under curve (AUC) metrics. These results highlight the framework's ability to capture complex nonlinearities and address class imbalance through RandomOverSampler. The findings demonstrate the potential of HHO-optimized models to advance automated threat detection, offering robust and scalable solutions for securing critical infrastructures.