Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 4
July-August 2026

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Cyber Sentinel AI: A Machine-Learning Framework for DDoS Detection Using Flow-Level Network Features

Author(s) Adhisha, L.H.Patil, Aftab Ali, Hiteshri, Aniket, Danish
Country India
Abstract Modern networks face increasingly sophisticated DDoS attacks that blend malicious traffic with legitimate flows, making detection difficult for traditional rule-based systems. This study introduces Cyber Sentinel AI, a machine-learning framework designed to classify network traffic by analysing structured features extracted from raw logs. The system converts IP addresses, ports, and protocol identifiers into numerical form, removes noise, and prepares a clean dataset suitable for model training. Supervised algorithms, particularly Random Forest and XGBoost, are trained on the CIC-DDoS2019 dataset to learn distinctive behavioural patterns separating normal and attack traffic. Experimental results show strong and stable performance, with accuracy typically ranging from 95 to 98 percent. Confusion-matrix analysis indicates minimal false alarms, while ROC curves and feature-importance scores highlight the relevance of IP- and port-based attributes in identifying malicious flows. The study demonstrates that ML models can capture subtle deviations in traffic behaviour more effectively than static security tools. The architecture is also flexible, enabling future real-time capabilities through streaming data pipelines, advanced anomaly detection models, and cloud-based deployment.
Keywords Cybersecurity, Distributed Denial-of-Service(DDoS), Machine Learning, Network Traffic Analysis, Random Forest Classifier, Intrusion Detection System, Feature Engineering.
Published In Volume 7, Issue 4, July-August 2026
Published On 2026-07-18
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E-ISSN 3048-7641
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