Using of Deep Learning Approaches For Denial of Services Attacks Detection in Asynchronous Transfer Mode Based Data Networks

Asynchronous Transfer Mode (ATM) technology was previously frequently utilized in high-speed networking to swiftly transmit a range of data formats using fixed-size cells. the packets network such as asynchronous transfer mode based TCP networks are developed t provide high speed data oscillation in which enhance the performance of many applications including broadband networks. This model is suspected to many networks attacks such as denial of service attacks. In this paper, we utilized CIC-DDoS2019 dataset to perform advanced detection model based on the artificial intelligence for detecting the denial of services attacks. Three models were in used namely convolutional neural network (the first paradigm), (CNN), Long short term memory neural network (the second paradigm) (LSTMNN) and Recurrent neural network (the third paradigm) (RNN). The results of this paper shown that long short memory neural network based paradigm is outperformed over the other models by providing an accurate detection of DoS attacks closed to 0.94.