Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural Network Article Swipe
G. M. Kadhar Nawaz
,
Muhammad Junaid
,
Adnan Akhunzada
,
Abdullah Gani
,
Shamyla Nawazish
,
Asim Yaqub
,
Adeel Ahmed
,
Huma Ajab
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2023.026952
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2023.026952
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/cmc.2023.026952
- https://www.techscience.com/cmc/online/detail/19510/pdf
- OA Status
- diamond
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388677083
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388677083Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32604/cmc.2023.026952Digital Object Identifier
- Title
-
Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
-
G. M. Kadhar Nawaz, Muhammad Junaid, Adnan Akhunzada, Abdullah Gani, Shamyla Nawazish, Asim Yaqub, Adeel Ahmed, Huma AjabList of authors in order
- Landing page
-
https://doi.org/10.32604/cmc.2023.026952Publisher landing page
- PDF URL
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https://www.techscience.com/cmc/online/detail/19510/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://www.techscience.com/cmc/online/detail/19510/pdfDirect OA link when available
- Concepts
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Denial-of-service attack, Computer science, Artificial neural network, Forwarding plane, Software-defined networking, Deep learning, Artificial intelligence, Feature (linguistics), Data mining, Machine learning, Computer network, Operating system, The Internet, Network packet, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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46Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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