Title
DDoSNet: A Deep-Learning Model for Detecting Network Attacks
Abstract
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.
Year
DOI
Venue
2020
10.1109/WoWMoM49955.2020.00072
2020 IEEE 21st International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)
Keywords
DocType
ISBN
CICDDoS2019 Dataset,DDoS attacks,Deep Learning,Intrusion Detection System,RNN,SDN
Conference
978-1-7281-7374-0
Citations 
PageRank 
References 
1
0.37
0
Authors
4
Name
Order
Citations
PageRank
Mahmoud Said Elsayed110.37
Nhien-An Le-Khac222449.63
Soumyabrata Dev36213.94
Anca Delia Jurcut411.04