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@article{196363,
        author = {V Prashanth and K Praveen and Shaik mohammed Bilal and M Harshavardan and D Mamatha},
        title = {CLASSIFICATION AND PREDICTION FOR DDOS ATTACKS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3441-3446},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196363},
        abstract = {With the rapid growth of internet services, Distributed Denial of Service (DDoS) attacks have emerged as a major threat to network security, causing service disruption, financial loss, and reputational damage. This project focuses on the classification and prediction of DDoS attacks using machine learning techniques. Network traffic data is collected, preprocessed, and transformed into relevant features for model training. Various classification algorithms, including Random Forest, Support Vector Machine (SVM), and Neural Networks, are applied to distinguish between normal and malicious traffic. Additionally, predictive models are implemented to anticipate potential attacks based on historical traffic patterns. The project emphasizes ethical privacy practices, including data anonymization and compliance with relevant regulations, ensuring sensitive user information is protected. Performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and detection latency. The results demonstrate the potential of machine learning-based systems in effectively detecting and predicting DDoS attacks, while maintaining privacy and fairness. This research contributes to enhancing network security by providing proactive and reliable DDoS mitigation strategies.},
        keywords = {},
        month = {April},
        }