Deep network approach with stacked sparse autoencoders in detection of DDoS attacks on SDN-based VANETInspec keywordsOther keywords

Polat H., Turkoglu M., Polat O.

IET COMMUNICATIONS, cilt.14, sa.22, ss.4089-4100, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 22
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1049/iet-com.2020.0477
  • Dergi Adı: IET COMMUNICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.4089-4100
  • Anahtar Kelimeler: computer network security, road safety, road traffic, neural nets, vehicular ad hoc networks, road accidents, intelligent transportation systems, software defined networking, traffic safety, SDN&#8208, based VANET systems, DDoS attacks, stacked sparse autoencoder, four&#8208, layer SSAE plus Softmax classifier deep network model, software&#8208, defined network&#8208, based vehicular ad hoc network, deep network approach, distributed denial of service attacks, smart transportation systems, traffic accidents, CLASSIFICATION
  • Gazi Üniversitesi Adresli: Evet

Özet

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.