AI-DRIVEN APPROACHES FOR PREVENTING CYBERSECURITY ATTACKS ON THE INTERNET

Abstract

The increasing frequency and sophistication of cyber attacks on the Internet pose a critical threat to global digital infrastructure. Traditional security methods, though essential, are often inadequate in detecting and mitigating advanced threats in real-time. This research explores the application of Artificial Intelligence (AI) techniques in the prevention of cyber security attacks on the Internet. The study investigates how AI algorithms—including machine learning, deep learning, and natural language processing—enhance threat detection, anomaly identification, phishing prevention, and malware classification. Emphasis is placed on supervised and unsupervised learning models that can analyze large volumes of network traffic and system logs to predict and respond to attacks dynamically. Real-time AI-based intrusion detection systems (IDS), behavior-based user authentication, and intelligent firewalls are discussed as part of a multi-layered security approach. Additionally, the study examines challenges in AI-driven security such as adversarial attacks, data quality issues, and the need for explainability in AI decisions. Through case studies and experimental evaluations, the research demonstrates the effectiveness of AI in proactively strengthening cyber security defenses and proposes a hybrid AI framework for adaptive Internet threat prevention.