Overview of Machine Learning Techniques in Cybersecurity Data Science using Gradient Boosting and Random Forest Algorithm

Over the past ten years, Internet of Things (IoT) technologies have expanded into a global monster that now engulfs every aspect of daily life by providing innumerable smart aids to human living. IoT faces more cybersecurity issues than ever because of its simplicity and growing demand for intelligent devices and networks. Therefore, a strong, up-to-date, and continuously enhanced cybersecurity approach is required for modern IoT devices. There has been a noticeable technological advancement in machine learning (ML), which has created several new research directions for addressing current and future IoT issues. The main goal of this investigation is to study an ML-based intrusion detection structure (IDS) for cybersecurity data science using a random forest (RF) and gradient boosting (GB) algorithm. Components for feature rankings and choosing, preprocessing, data search, and standardizing are all incorporated in Optimized Gradient Boost and Random Forest (OGBRF-IDS). The empirical findings in the present research show that the suggested approaches enhance cyber intrusion identification for both novel and old scenarios. Performance assessments show that the suggested IDS (GBRF) outperforms conventional ML techniques. The UNBS-NB 15, CICIDS2018, and KDD 99 databases are used for performance evaluation. The suggested IDS can forecast cyberattacks in every database in the shortest amount of period and has the finest attack identification ratios.