Intrusion Detection System Using Decision Tree Algorithm: A Machine Learning Approach

This repository contains the research paper and dataset for the study titled "Intrusion Detection System Using Decision Tree Algorithm: A Machine Learning Approach". The study presents a machine learning-based Intrusion Detection System (IDS) using the CART Decision Tree algorithm. A synthetic network traffic dataset containing 500 records was used for model development and evaluation. The dataset includes features such as Packet Size Average, Connection Duration, Packet Rate per Second, Number of Ports Used, Payload Entropy, and Anomaly Score. The Decision Tree classifier achieved: • Test Accuracy: 88%• Precision: 0.9375• Recall: 0.9036• F1-Score: 0.9202• ROC-AUC: 0.8047 The uploaded files include:1. Research Paper (PDF)2. Dataset (CSV) This work was conducted as part of MCA academic research at Chanakya University, Bengaluru, India.