Deteksi Serangan Web defacement pada Infrastruktur Kritis Menggunakan Machine Learning

Abstract

The number of threats to the security of websites and web servers, which include things like web defacement, is increasing every year. This is a major concern in today's cybersecurity world. It includes websites that are part of critical infrastructure, like government, health, and energy systems. This research study looks at how machine learning (ML) models can automatically detect web defacement attacks. The main goal is to make sure these models are very accurate. We developed a supervised learning-based classification model using web defacement datasets from public archives and simulated mock sites. This research study looks at how well three types of classification models—Random Forest, support vector machine (SVM), and naive Bayes—perform at identifying defaced websites. The results of the experiment show that Random Forest is the best option, with up to 96% accuracy. This research shows that the Machine Learning (ML) approach could be very important in developing a system that can detect cyberattacks early on. This system would protect important infrastructure in the country.