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@article{175721,
        author = {Isaivani Mathiyalagan},
        title = {Enhanced ddos attack detection  using radial basis function network},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3587-3592},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175721},
        abstract = {Distributed Denial of Service (DDoS) attacks represent a significant threat to network security, disrupting services and causing financial and reputational losses. Traditional detection methods often struggle to adapt to evolving attack patterns, leading to delayed responses and compromised system performance. This project aims to enhance DDoS attack detection by leveraging the Radial Basis Function Network (RBFN), a type of artificial neural network renowned for its ability to handle nonlinear data patterns effectively. The proposed solution involves the design and implementation of an RBFN-based model capable of identifying and mitigating DDoS attacks with high accuracy and minimal latency. The RBFN's architecture is optimized to process large-scale network traffic data, learning complex patterns that distinguish between legitimate and malicious traffic. Extensive experimentation will validate the system's performance using real-world datasets, evaluating metrics such as detection accuracy, false positive rate, and response time. The results are expected to demonstrate a significant improvement over conventional machine learning models, showcasing the RBFN's potential in DDoS attack detection and mitigation.},
        keywords = {DDoS Attack Detection, Radial Basis Function Network, Network Security, Intrusion Detection Systems.},
        month = {April},
        }