NETWORK TRAFFIC ANALYSIS FOR INTRUSION DETECTION USING PYTHON

Abstract

Intrusion detection in network traffic is vital for cybersecurity, and traditional rule-based methods often fall short against evolving threats. This study leverages Machine Learning (ML) techniques using Python—specifically Regression, K-Nearest Neighbours (KNN), and Decision Trees (DT)—to enhance anomaly detection. Regression identifies unusual traffic variations, KNN classifies activities based on similarity, and DTs improve accuracy by learning from past attacks. By processing real-time network data and applying these models, the approach achieves higher accuracy and efficiency than conventional methods, emphasizing the crucial role of ML in modern cybersecurity defenses.