CREATION AND IMPLEMENTATION OF A PROGRAM THAT DETECTS AND ELIMINATES NETWORK ATTACKS BASED ON ARTIFICIAL INTELLIGENCE

Abstract

The rapid digital transformation of modern society has significantly increased the scale and complexity of cyber threats. Traditional signature-based security systems are no longer sufficient to counter sophisticated and adaptive network attacks. This article explores the creation and implementation of an intelligent program for detecting and eliminating network attacks based on artificial intelligence (AI). The proposed system integrates machine learning algorithms, anomaly detection models, and automated response mechanisms to ensure real-time protection of network infrastructures. Special attention is given to system architecture, data preprocessing, model training, and deployment stages. Practical examples of attack detection, including Distributed Denial-of-Service (DDoS), phishing attempts, and intrusion activities, are analyzed to demonstrate the effectiveness of AI-driven solutions. The research highlights the advantages of adaptive learning models over traditional cybersecurity approaches and discusses challenges such as false positives, data imbalance, and computational costs. The implementation results confirm that AI-based systems significantly improve detection accuracy, reduce response time, and enhance overall network resilience.