TY - JOUR TI - DDoS detection and mitigation using machine learning DO - https://doi.org/doi:10.7282/T3HX1H13 PY - 2018 AB - Distributed Denial of Service (DDoS) attacks are very common nowadays. It is evident that the current industry solutions, such as completely relying on the In- ternet Service Provider (ISP) or setting up a DDoS defense infrastructure, are not sufficient in detecting and mitigating DDoS attacks, hence consistent research is needed. In this thesis we first tried to understand how DDoS attacks happen, then we discussed a way to detect DDoS attacks using machine learning tools at the routers, instead of setting up a centralized analysis system. We have proposed a standard communication architecture which can be used across all the networking devices for mitigating DDoS attacks. We have also created a simulation program to demonstrate our detection technique. KW - Computer Science KW - Machine learning KW - Denial of service attacks LA - eng ER -