DDoS Attack Detection- Using AIML Techniques
Year: 2025
Type: article
Abstract: Distributed Denial of Service (DDoS) attacks have emerged as a major threat to network security, causing significant disruptions to online services and substantial financial losses for organizations. This research investigates the applications of various machine learning techniques for DDoS attacks detection, utilizing the CICDDoS2019 dataset, which involves a wide spectrum of attack types and standard traffic patterns. We evaluate three prominent machine learning algorithms: Random Forest, XGBoost, and LightGBM. Our findings indicate that these models can effectively classify DDoS traffic with high accuracy, although performance varies across different attack types. The study emphasizes the significance of feature selection, data preprocessing, and model evaluation in enhancing detection capabilities. This research contributes to ongoing efforts to improve cyber security measures against evolving DDoS threats.
Authors: Baral, Manoranjan, Rane, Shriya, Kaur, Dr. Jasbir, Kampoo, Prof. Ifrah, Thakkar, Prof. Sandhya
Institution: Guru Nanak Institute of Management Studies
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W6930720879
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