Detection of Network Traffic Analyzer Website Using Machine Learning Algorithm

In the ever-evolving landscape of cybersecurity, timely identification of cyberattacks is critical for protecting digital infrastructures. This research introduces a system that applies supervised machine learning techniques to classify and predict different types of cyber threats, including malware, phishing, and brute-force attacks. Using a comprehensive dataset of past attack instances, the model learns to recognize patterns and features that distinguish each type of attack. The study explores the performance of various supervised learning algorithms such as Decision Trees, Support Vector Machines (SVM), and Neural Networks to identify the most accurate and reliable approach for threat detection. To ensure continued effectiveness, the model is regularly updated to adapt to emerging threats. Combined with tools like the confusion matrix for performance evaluation, the proposed system provides cybersecurity teams with a practical solution for identifying threats early and responding swiftly, ultimately strengthening the security posture of digital systems. Key Words: Supervised machine learning, Cyberattack classification, Dataset, Support Vector Machines (SVM), Neural Networks, Confusion Matrix, Threat mitigation.