A Stacking Enriched Explainable Machine Learning Approach for Multiclass DoS Attack and Network Intrusion Prediction

Abstract

DoS attack and intrusion prediction is essential for IoT networks because it guarantees service availability, and preserves reputation by reducing expensive downtime. Benign, infiltration traffic, DoS golden eye, hulk, slowloris, and slow http test classes were all not taken at the same time with high accuracy in earlier studies on DoS attack and intrusion detection. This research presents a stacking augmented explainable machine learning method for predicting multiclass DoS assaults and network intrusions. The relevant dataset was gathered from the Kaggle source and verified by cyber specialists. Chi-square, RFE, and mutual information are used in feature selection along with SMOTE-based data balance and preprocessing. SHAP is utilized for model explain ability, while grid search CV is used for hyper parameter tweaking. The best DoS attack and intrusion prediction model selection is examined using RF, XGBoost, catboost, LGBM, LR, and ensemble stacking technique (RF, XGB, LR). The stacking enriched ensemble strategy is chosen as the best prediction model in this paper with an accuracy of 97.953 percent. The accuracy of the suggested method is more than 1.75 percent higher than that of previous studies.