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@article{172361,
        author = {ATHISHA L and Kalpana sri K and Kaniga K and Keerthana R},
        title = {DDoS Protection System Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {3376-3381},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172361},
        abstract = {A Distributed Denial of Service (DDoS) attack disrupts service availability by devastating a server or network with massive volumes of circulation, often from several sources, making it tough or impossible for the genuine users to admission the service. Given the growth complexity and diversity of DDoS attacks, traditional methods often struggle to exactly detect and mitigate these attacks, especially when new or complex patterns emerge. This paper introduces a DDoS protection system focused not only on detecting and mitigating active threats but also on analysing past attack patterns to predict potential future threats. By investigating traffic behaviour, attack signatures, and other indicators, our system identifies patterns and generates understandings on the types of attacks that may be likely to occur. This proactive approach allows users to take preventive measures, enhancing their overall defence against DDoS threats. Our solution exploits advanced techniques, including machine learning and behavioural analysis, to study historical traffic data and developing patterns in real-time. This enables the system to differentiate between normal traffic flows and signs of an imminent attack. Additionally, we benchmark our system against leading DDoS protection tools to showcase its advantages in terms of predictive accuracy, detection speed, and response efficacy. Our findings specify that our approach significantly outstrips existing solutions, offering an enhanced level of protection that shifts from reactive defence to a more proactive and pre-emptive security model. This research ultimately demonstrates how analysing historical attack data can help predict future threats, equipping users with an effective and forward-looking tool to safeguard their online services from the growing risk of DDoS attacks.},
        keywords = {Distributed Denial of Facility, Domain Name System, Internet Control Communication Protocol, Interruption Detection System, Time to Living, User Datagram Protocol.},
        month = {January},
        }