Application of C4.5 Decision Tree Algorithm for Detecting Cyber Attacks Using IDS

Abstract

Network infrastructure faces mounting pressure from increasingly sophisticated cyber attacks targeting systems reliant on digital connectivity. Activities such as Denial of Service, Probe, Remote-to-Local, and User-to-Root represent persistent threats that are challenging to intercept manually owing to high traffic volumes and rapidly evolving intrusion strategies. This work constructs a web-based Intrusion Detection System prototype by training an entropy-based Decision Tree classifier, conceptually grounded in the C4.5 framework, on the NSL-KDD benchmark. Development adheres to the six-phase CRISP-DM process encompassing business understanding, data preparation, model construction, performance evaluation, and system deployment. Preprocessing involves duplicate record elimination, categorical attribute encoding, and consolidation of multi-class attack labels into a binary Normal/Anomaly scheme. Ten-fold cross-validation yields 99.34% accuracy and 99.35% precision for the Decision Tree, representing a substantial margin over Naive Bayes which attains only 66.67% accuracy. A separate Gain Ratio analysis designates src_bytes as the most informative feature with a value of 0.5366, positioning it as the decision tree root node. The prototype is served through a Streamlit web application that accepts CSV dataset uploads, executes batch anomaly classification, presents detection outcomes through interactive visualizations, and produces exportable reports in both CSV and PDF formats. The system offers an interpretable and practically accessible batch-detection tool, continuous real-time monitoring and evaluation on contemporary intrusion benchmarks are identified as priorities for subsequent research.