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Cited by 57Network Security and Intrusion DetectionVehicular Ad Hoc Networks (VANETs)Adversarial Robustness in Machine Learning

The DOI matches its Crossref record.

Deep network approach with stacked sparse autoencoders in detection of DDoS attacks on SDN‐based VANET

Polat, Huseyin, Turkoglu, Muammer, Polat, Onur·2020·IET Communications
DOI 10.1049/iet-com.2020.0477
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Abstract

Software‐defined network (SDN)‐based vehicular ad hoc network (VANET) is an outstanding technology for smart transportation as it increases traffic safety, efficiency, comfort, and manageability. However, despite all its benefits and good performance, SDN‐based VANET is vulnerable to attack threats such as distributed denial of service (DDoS). When SDN‐based VANET systems are exposed to DDoS attacks, this may affect traffic safety, causing traffic accidents and deaths. Therefore, the relevant security threats need to be addressed before integrating the SDN‐based VANETs into smart transportation systems. In this study, the stacked sparse autoencoder (SSAE) + Softmax classifier deep network model is proposed to detect DDoS attacks targeting SDN‐based VANETs. The features in the dataset obtained from the SDN‐based VANET were reduced dimensionally utilising SSAE, and the most significant features were obtained. Then, these features were used as input into the Softmax classifier. According to the experimental results, the best accuracy scores were calculated as 96.9% using the four‐layer SSAE + Softmax classifier deep network model proposed. When compared, the results demonstrate the SSAE + Softmax classifier deep network model proposed can obtain better results in the classification of DDoS attacks and is more successful than the other machine learning classifiers.

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Metadata

Authors
Polat, H., Turkoglu, M., & Polat, O.
Affiliations
  • Department of Computer Engineering Faculty of Technology Gazi University Ankara 06500 Turkey
  • Department of Computer Engineering Bingol University Bingol 12400 Turkey
  • Gazi University (TR)
Corresponding author
Polat, Onur
ORCID
Venue
IET Communications
Publisher
Institution of Engineering and Technology (IET)
Published
2020-12
Volume
14
Issue
22
Pages
4089-4100
ISSN
  • 1751-8628 (ISSN-L)
  • 1751-8636
DOI
10.1049/iet-com.2020.0477
OpenAlex
W3131556260
Cited by
57
References
0
Citations by year
2026: 10 · 2025: 14 · 2024: 15 · 2023: 8 · 2022: 10
FWCI
2.11
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 77 d)
Locations
IET Communications (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Keywords
DDoS attack detection, software-defined networking, vehicular ad hoc networks, stacked sparse autoencoder, softmax classifier, deep learning, dimensionality reduction, network security, smart transportation

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CoChat. (2026). Citation check: Deep network approach with stacked sparse autoencoders in detection of DDoS attacks on SDN‐based VANET. https://app.cochat.ai/papers/10.1049/iet-com.2020.0477
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Polat, H., Turkoglu, M., & Polat, O. (2020). Deep network approach with stacked sparse autoencoders in detection of DDoS attacks on SDN‐based VANET. IET Communications. https://doi.org/10.1049/iet-com.2020.0477

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