MSOM: Efficient Mechanism for Defense against DDoS Attacks in VANET

• Published in: Wireless communications and mobile computing 2021, Vol.2021 (1)
• Main Authors: Al-Mehdhara, Mohammed, Ruan, Na
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Communication; Computer architecture; Cybersecurity; Data mining; Defense mechanisms; Denial of service attacks; Investigations; Machine learning; Mobile ad hoc networks; Multilayers; Network reliability; Neural networks; Safety management; Security management; Security services; Self organizing maps; Software; Software-defined networking; Traffic congestion; Traffic management; Traffic safety; Traffic speed; Vehicles; Wireless networks
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2021/8891758

The wireless nature of the Vehicular Ad Hoc Network (VANET), a technology that offers facilities such as traffic management and safety services, makes it vulnerable to distributed denial-of-service (DDoS) attacks that exploit network communications and reduce network reliability and performance. This paper proposes a design of a secure VANET architecture using a Software-Defined Networking (SDN) controller and Neural Network Self-Organizing Maps (SOMs). In the proposed design, we adopt the SDN architecture by using its separation of the control plane from the data plane and adding intelligent capabilities to the VANET. To resolve the drawbacks of standard SOMs and to enhance the SOM’s efficiency, a Multilayer Distributed SOM (MSOM) model based on two levels of clustering and classification is used. Experimental results show that our solution can efficiently detect malicious traffic, prevent and mitigate DDoS attacks, and increase system security and recovery speed from the attacking traffic. Moreover, the proposed scheme achieves a high accuracy rate (99.67%). Simulation results demonstrate the effectiveness and efficiency of the MSOM regarding detection accuracy and other studied metrics.