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@article{206672,
        author = {Roushan Kumar and Dr. Jasdeep Singh},
        title = {An Intrusion Detection System for DDoS Attacks Classification Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {2530-2539},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206672},
        abstract = {Distributed Denial of Service (DDoS) attacks are among the most critical threats to modern computer networks, causing service disruption and significant performance degradation. Traditional intrusion detection systems often struggle to identify sophisticated and evolving attack patterns. Machine Learning (ML) techniques provide intelligent and data-driven approaches for detecting malicious network activities. This paper presents a Machine Learning-Based Intrusion Detection System (IDS) for DDoS attack classification using the UNSW-NB15 dataset. Three machine learning algorithms, namely Decision Tree, Random Forest, and XG Boost, were implemented and evaluated. The dataset was preprocessed through categorical feature encoding and stratified train-test splitting. Model performance was assessed using Accuracy, Precision, Recall, F1-Score, Confusion Matrix, Receiver Operating Characteristic (ROC) Curve, Area Under the Curve (AUC), and 5-Fold Cross Validation. Experimental results show that the Random Forest classifier achieved the best overall performance with an accuracy of 95.13%, F1-score of 96.46%, AUC score of 0.990, and a cross-validation accuracy of 95.20%. XG Boost achieved the highest recall of 98.22%, indicating strong attack detection capability. The findings demonstrate that machine learning-based intrusion detection systems can effectively distinguish malicious traffic from legitimate network traffic and provide a reliable solution for DDoS attack classification.},
        keywords = {DDoS Attack, Intrusion Detection, Machine Learning, Network Security, Random Forest, UNSW-NB15, XG Boost.},
        month = {July},
        }