  Software-Defined Networking (SDN) is an emerging paradigm, which evolved in
recent years to address the weaknesses in traditional networks. The significant
feature of the SDN, which is achieved by disassociating the control plane from
the data plane, facilitates network management and allows the network to be
efficiently programmable. However, the new architecture can be susceptible to
several attacks that lead to resource exhaustion and prevent the SDN controller
from supporting legitimate users. One of these attacks, which nowadays is
growing significantly, is the Distributed Denial of Service (DDoS) attack. DDoS
attack has a high impact on crashing the network resources, making the target
servers unable to support the valid users. The current methods deploy Machine
Learning (ML) for intrusion detection against DDoS attacks in the SDN network
using the standard datasets. However, these methods suffer several drawbacks,
and the used datasets do not contain the most recent attack patterns - hence,
lacking in attack diversity.
  In this paper, we propose DDoSNet, an intrusion detection system against DDoS
attacks in SDN environments. Our method is based on Deep Learning (DL)
technique, combining the Recurrent Neural Network (RNN) with autoencoder. We
evaluate our model using the newly released dataset CICDDoS2019, which contains
a comprehensive variety of DDoS attacks and addresses the gaps of the existing
current datasets. We obtain a significant improvement in attack detection, as
compared to other benchmarking methods. Hence, our model provides great
confidence in securing these networks.
