AUTOMATED INTRUSION DETECTION IN NETWORK TRAFFIC USING AI

Abstract

This report examines how Artificial Intelligence (AI) and Machine Learning (ML) can be applied to automate the analysis of network traffic in order to improve the detection of cyber threats. The study is based on the increased need for scalable security solutions, due to growing data volumes and an increasingly complex threat landscape, where traditional manual analysis methods exhibit significant limitations. The aim is to analyze how AI and ML techniques can be used to identify patterns and anomalies in raw network traffic, and to evaluate the extent to which these techniques can replace or complement manual threat hunting. The study also aims to highlight the opportunities and limitations associated with automated threat detection in operational cybersecurity environments.