In light of the escalating ubiquity of the Internet, the proliferation of cyber-attacks, coupled with their intricate and surreptitious nature, has significantly imperiled network security. Traditional machine learning methodologies inherently exhibit constraints in effectively detecting and classifying multifarious cyber threats. Specifically, the surge in high-dimensional network traffic data and the imbalanced distribution of classes exacerbate the predicament of ideal classification performance. Notably, the presence of redundant information within network traffic data undermines the accuracy of classifiers. To address these challenges, this study introduces a novel approach for intrusion detection classification which integrates advanced techniques of feature engineering and model optimization. The method employs a feature engineering approach that leverages mutual information maximum correlation minimum redundancy (mRMR) feature selection and synthetic minority class oversampling technique (SMOTE) to process network data. This transformation of raw data into more meaningful features effectively addresses the complexity and diversity inherent in network data, enhancing classifier accuracy by reducing feature redundancy and mitigating issues related to class imbalance and the detection of rare attacks. Furthermore, to optimize classifier performance, the paper applies the Optuna method to fine-tune the hyperparameters of the Catboost classifier, thereby determining the optimal model configuration. The study conducts binary and multi-classification experiments using publicly available datasets, including NSL_KDD, UNSW-NB15, and CICIDS-2017. Experimental results demonstrate that the proposed method outperforms traditional approaches regarding accuracy, recall, precision, and F-value. These findings highlight the method’s potential and performance in network intrusion detection.

To address the challenges posed by high data dimensionality and class imbalance during intrusion detection, which result in increased computational complexity, resource consumption, and reduced classification accuracy, this paper presents an intrusion-detection algorithm based on an improved Random Forest approach. The algorithm employs the Bald Eagle Search (BES) optimization technique to fine-tune the Kernel Principal Component Analysis (KPCA) algorithm, enabling optimized dimensionality reduction. The processed data are then fed into a cost-sensitive Random Forest classifier for training, with subsequent model validation conducted on the reduced-dimension data. Experimental results demonstrate that compared to traditional Random Forest algorithms, the proposed method reduces the training time by 11.32 s and achieves a 5.59% increase in classification accuracy, an 11.7% improvement in specificity, and a 0.0558 increase in the G-mean value. These findings underscore the promising application potential and performance of this approach in the field of network intrusion detection.