Enhancing Web Security with a Blockchain-Powered Machine Learning Model for Predicting Malicious Web Domains

Introduction the purpose of this introductory section is to provide an overview of the topic at hand and set in the context of the dynamic and ever-changing digital era, the issue of web security continues to be of utmost importance, as rogue web domains pose substantial risks to both individuals and institutions. This study presents a novel methodology for addressing this issue by combining blockchain technology with machine learning techniques, notably employing the Support Vector Machine (SVM) and Principal Component Analysis (PCA), for the purpose of forecasting and categorizing malevolent internet domains. The proposed architecture leverages the decentralized and immutable characteristics of blockchain technology to ensure the integrity and reliability of the acquired web domain data. Furthermore, the utilization of the blockchain architecture enables the seamless exchange of data in real-time, resulting in a perpetually updated and resilient model. Principal Component Analysis (PCA) is utilized in order to decrease the dimensionality of the information, hence enabling more efficient processing and emphasizing the most pertinent features. As a result, the Support Vector Machine (SVM) is employed as the predominant predictive model owing to its established efficacy in addressing binary classification tasks. The findings from our experiment demonstrate that the SVM model, enhanced by blockchain technology and feature optimization using PCA, had greater performance in the identification of harmful domains when compared to conventional models. This was evident through a significant improvement in accuracy and a decrease in false positive rates. This technique not only enhances the robustness of web security infrastructure, but also showcases the possibility of integrating blockchain technology with machine learning algorithms to effectively tackle modern cyber threats.