Hybrid AI-Powered Real-Time Distributed Denial of Service Detection and Traffic Monitoring for Software-Defined-Based Vehicular Ad Hoc Networks: A New Paradigm for Securing Intelligent Transportation Networks

Polat O., Oyucu S., Türkoğlu M., POLAT H., AKSÖZ A., Yardımcı F.

Applied Sciences (Switzerland), cilt.14, sa.22, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 22
  • Basım Tarihi: 2024
  • Doi Numarası: 10.3390/app142210501
  • Dergi Adı: Applied Sciences (Switzerland)
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: DDoS attack, deep learning, intelligent transportation systems, real-time intrusion detection, SDN, VANET
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

Vehicular Ad Hoc Networks (VANETs) are wireless networks that improve traffic efficiency, safety, and comfort for smart vehicle users. However, with the rise of smart and electric vehicles, traditional VANETs struggle with issues like scalability, management, energy efficiency, and dynamic pricing. Software Defined Networking (SDN) can help address these challenges by centralizing network control. The integration of SDN with VANETs, forming Software Defined-based VANETs (SD-VANETs), shows promise for intelligent transportation, particularly with autonomous vehicles. Nevertheless, SD-VANETs are susceptible to cyberattacks, especially Distributed Denial of Service (DDoS) attacks, making cybersecurity a crucial consideration for their future development. This study proposes a security system that incorporates a hybrid artificial intelligence model to detect DDoS attacks targeting the SDN controller in SD-VANET architecture. The proposed system is designed to operate as a module within the SDN controller, enabling the detection of DDoS attacks. The proposed attack detection methodology involves the collection of network traffic data, data processing, and the classification of these data. This methodology is based on a hybrid artificial intelligence model that combines a one-dimensional Convolutional Neural Network (1D-CNN) and Decision Tree models. According to experimental results, the proposed attack detection system identified that approximately 90% of the traffic in the SD-VANET network under DDoS attack consisted of malicious DDoS traffic flows. These results demonstrate that the proposed security system provides a promising solution for detecting DDoS attacks targeting the SD-VANET architecture.