In recent years, Software Defined Networking (SDN) has gained traction as an innovative approach to overcome the limitations of legacy systems. The SDN's main goal is to separate the control and data planes, making network management easier and enabling for more efficient programmability. The suggested approach would protect the SDN in a more accurate, effective, and scalable manner, overcoming current methods' shortcomings. Different businesses employ network detection algorithms to arrange and differentiate harmful traffic these days, however there may be many challenges to encounter and determine assaults in imbalanced datasets. Examining the influence of different contemporary Deep Learning (DL) approaches, such as the Long Short-Term Memory (LSTM) based autoencoder and the Recurrent Neural Network (RNN), on the overall system functioning would increase the productivity of the anomaly-based Intrusion Detection System (IDS).