Dissertation (MA Science and Telecommunication Engineering)Recently, the advancement of technology and internet contributes to the increase of the network traffic over the globe. It improves digital services delivery over the global network such as online shopping, television, and streaming. However, as digital services become one of the de facto applications over the internet, a number of attacks on them have been increasing which raise security concerns. Some of the major attacks are Distributed Denial of Service (DDoS) and Flash Events (FE). One hand DDoS attacks mainly focus on disrupting the legitimate users to access the internet. On the other side, FE occurs when there is a rapid growth of legitimate users that access the service over the internet and overload the system. DDoS attacks and FE have similar behaviour however, they need different countermeasures. The major challenge lies in detection the attacks especially when DDoS and FE happen simultaneously. The study proposed a model to detect the FE and DDoS attacks when occurring simultaneously in network traffic using deep learning techniques with three different hidden layers and two optimizers. Validations of the models were tested with data from the real network traffics and the model with high performance was selected which was a model with three hidden layer and Adam optimizer. The result shows a proposed model achieved a good accuracy of99% and less than 1% false alarm.