ARTIFICIAL INTELLIGENCE BASED NETWORK TRAFFIC ANOMALY DETECTION

Abstract

This paper explores the use of artificial intelligence to detect anomalies in network traffic and strengthen cybersecurity. The study analyzes AI methods that automate and enhance the identification of abnormal patterns indicating possible threats. Using intelligent algorithms to process network data, the research aims to increase detection accuracy, reduce false positives, and improve response speed. Results show that AI-based solutions outperform traditional approaches in adaptability and efficiency. The findings highlight the importance of integrating advanced AI techniques into network monitoring to address growing cybersecurity challenges.