Optimizing Intrusion Detection for DoS, DDoS, and Mirai Attacks Subtypes Using Hierarchical Feature Selection and CatBoost on the CICIoT2023 Dataset

Introduction: Modern networks suffer until unheard of vulnerabilities that need for advanced intrusion detection systems (IDS) given the growing danger presented by DoS, DDoS, and Mirai attacks. Research on the identification of certain attack subtypes is still lacking even with the CICIoT2023 dataset, which offers a complete basis for evaluating these cyber hazards. Usually, aggregating attacks into more general categories, existing research neglects the complex characteristics of specific subtypes, therefore reducing the detection effectiveness. Methods: This work presents a novel IDS model aiming at high accuracy detection of DoS, DDoS, and Mirai attack subtypes. Using hierarchical feature selection and the CatBoost algorithm on the CICIoT2023 dataset, our model addresses the problems of high-dimensional data and emphasizes on keeping the most important features by means of advanced preprocessing methods including Spearman correlation and hierarchical clustering. Furthermore, used is stratified sampling to guarantee in the training and testing stages fair representation of attack types, both common and uncommon. Results: With an amazing Prediction Time per Network Flow of 7.16e-07 seconds, our model shows a breakthrough in intrusion detection performance by means of rigorous stratified cross-valuation, thereby attaining outstanding outcomes in accuracy, recall, and precision. Conclusions: Our method not only closes a significant gap in current knowledge but also establishes a new benchmark in cybersecurity by providing very detailed protection mechanisms against advanced threats. This study marks major progress in network security as it gives companies a more efficient instrument to recognize and minimize certain cyber risks with better precision and effectiveness