Denial of Service Attacks Prevention using Traffic Pattern Recognition over Software-Defined Network

Recent trends have shown a migration of software from local machines to server-based services. These service-based networks depend on high up-times and heavy resistance in order to compete in the market. Along with this growth, denial of service attacks have equally grown. Defending against these attacks has become increasingly difficult with the growth of Internet of Things and the different varieties of denial of service attacks. For this, our research offers a solution of implementing software-defined networking and real-time metric based techniques to mitigate a denial of service attack within a smaller time window than other comparable solutions. The use of our method offers both efficient attack handling and also flexibility to fit a variety of implementations. The end result being a network that can automatically adapt against new attacks based on previous network activity.