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@article{171514,
        author = {Shreya G and Suchan M B and Shreya and Tazeen R A R and Mrs Prema Jain},
        title = {Machine Learning - Based Classification Techniques for DDoS Attacks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {469-477},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171514},
        abstract = {Cybersecurity threats are evolving at an unprecedented rate, with Distributed Denial of Service (DDoS) attacks standing out as one of the most disruptive forms. These attacks flood target networks with a surge of malicious traffic, exhausting their resources and making them inaccessible to legitimate users. The widespread availability of tools for launching DDoS attacks has further amplified their prevalence, posing significant challenges to individuals, organizations, and critical infrastructure worldwide. As attackers employ increasingly sophisticated techniques, the need for robust and adaptable defense mechanisms has become paramount. Traditional methods of DDoS detection rely heavily on predefined rules and signature-based systems. While effective against known attack patterns, these approaches often fall short in identifying novel or evolving threats. The dynamic nature of modern DDoS attacks demands a solution capable of learning and adapting in real time.},
        keywords = {DDoS Attack, Machine Learning, Classification, Prediction.},
        month = {January},
        }