Enhanced Ransomware Detection Using Gradient Boosting Algorithms: A Cybersecurity Dataset Approach

Within the constantly changing field of cyber security, ransomware attacks represent a serious risk to individuals and enterprises. Mitigating the harmful impacts of these attacks requires early and accurate detection and technology. Using a large cybersecurity dataset, this study investigates the use of learning models specifically, Gradient Boosting Machine (GBM), Adaptive Gradient Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) for the detection of ransomware attacks. The dataset has a wealth of variables that define both benign and malevolent activity, making it an excellent resource for training and assessment. Preparing the dataset to address missing values, normalizing the data, and selecting features are all methods to improve model performance. Next, classification models are constructed using the GBM, AdaBoost, and XGBoost algorithms, each of which makes use of its special advantages in managing intricate patterns and relationships found in the data. The models’ performance is evaluated through the use of common metrics like F1 score, accuracy, precision, and recall. According to preliminary findings, the XGBoost model performs better than the others in terms of robustness and detection accuracy, proving its capacity to differentiate between legitimate and ransomware activity.