Intelligent Cyber Attack Prediction Using Data-Driven Machine Learning Models for Enhanced Network Security

Abstract

The rapid growth of internet technologies and digital communication platforms has resulted in a substantial increase in cybersecurity risks and cyberattack incidents. Conventional security systems that depend on static, rule-based mechanisms are often inadequate for identifying and preventing modern, dynamic, and sophisticated threats. To overcome these challenges, this paper introduces an intelligent cyber-attack prediction framework based on data-driven machine learning techniques aimed at strengthening network security. The proposed system processes user-submitted URLs along with associated communication protocols to assess potential security vulnerabilities. A Flask-based web application is developed to support real-time interaction between users and the predictive model. Machine learning algorithms are applied to analyze URL behavior and classify them as either safe or malicious. The system further categorizes threats into low, medium, and high risk levels while generating automated mitigation recommendations. All prediction results are stored in a database to enable visualization and trend analysis. This approach enhances detection accuracy, minimizes manual intervention, and supports proactive cybersecurity defense mechanisms.