@Article{cmc.2023.026952,
AUTHOR = {Gul Nawaz, Muhammad Junaid, Adnan Akhunzada, Abdullah Gani, Shamyla Nawazish, Asim Yaqub, Adeel Ahmed, Huma Ajab},
TITLE = {Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {77},
YEAR = {2023},
NUMBER = {2},
PAGES = {2157--2178},
URL = {http://www.techscience.com/cmc/v77n2/54785},
ISSN = {1546-2226},
ABSTRACT = {Distributed denial of service (DDoS) attack is the most common attack that obstructs a network and makes it
unavailable for a legitimate user. We proposed a deep neural network (DNN) model for the detection of DDoS
attacks in the Software-Defined Networking (SDN) paradigm. SDN centralizes the control plane and separates
it from the data plane. It simplifies a network and eliminates vendor specification of a device. Because of this
open nature and centralized control, SDN can easily become a victim of DDoS attacks. We proposed a supervised
Developed Deep Neural Network (DDNN) model that can classify the DDoS attack traffic and legitimate traffic.
Our Developed Deep Neural Network (DDNN) model takes a large number of feature values as compared to
previously proposed Machine Learning (ML) models. The proposed DNN model scans the data to find the
correlated features and delivers high-quality results. The model enhances the security of SDN and has better
accuracy as compared to previously proposed models. We choose the latest state-of-the-art dataset which consists of
many novel attacks and overcomes all the shortcomings and limitations of the existing datasets. Our model results
in a high accuracy rate of 99.76% with a low false-positive rate and 0.065% low loss rate. The accuracy increases to
99.80% as we increase the number of epochs to 100 rounds. Our proposed model classifies anomalous and normal
traffic more accurately as compared to the previously proposed models. It can handle a huge amount of structured
and unstructured data and can easily solve complex problems.},
DOI = {10.32604/cmc.2023.026952}
}