PREVIR: Fortifying Vehicular Networks Against Denial of Service Attacks

• Published in: IEEE access 2024, Vol.12, p.48301-48320
• Main Authors: Verma, Amandeep, Saha, Rahul, Kumar, Gulshan, Conti, Mauro, Kim, Tai-Hoon
• Format: Article
• Language: English
• Subjects: Accuracy; attack; Computer crime; Cybersecurity; Datasets; DDoS; Denial of service attacks; Denial-of-service attack; Floods; Internet of Things; Logistic regression; Logit models; Machine learning; Networks; prevention; Probabilistic analysis; Risk assessment; Security; Statistical analysis; VANETs; Vehicles; Vehicular; Vehicular ad hoc networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3382992

Vehicular networks are expanding their applications for future sustainability. The reported increasing rate of data breaches through vehicular networks by Distributed Denial of Service (DDoS) type of intrusion creates concern for such networks. Existing security solutions focus only on intrusion detection. However, prevention solutions are more proactive and provide security by probabilistic analysis. Existing prevention models for vehicular networks have low accuracy and are unable to handle zero-day attacks and advanced persistent threats. In this paper, we solve the problems mentioned above and introduce Predictive Risk Evaluation for Vehicular Infrastructure Resilience (PREVIR), the first amalgamated model of logit method (statistical analysis) and LogitBoost method (machine learning) to prevent DDoS attacks in vehicular networks. In PREVIR, the logit model predicts the packet probabilities for identifying maliciousness. The machine learning method improves PREVIR's performance through iterative refinement of the model's periodic updates based on new traffic parameters. We run a set of experiments on PREVIR. We use our NS3-generated dataset, NSL-KDD public dataset, and CIC-DDoS public dataset. PREVIR analyses multiple attack types, including UDP flood, TCP flood, mixed flooding, U2R, Probe, and R2L attacks. The results show that PREVIR classifies packets with accuracy up to 99.99%. Our proposed PREVIR model achieves a True Positive Ratio (TPR) up to 100% and an average False Positive Ratio (FPR) of 35%. The comparative analysis shows that PREVIR's efficiency is 20% better on average in the prevention of malicious packets as compared to the state-of-the-art models.