PETRAK: A solution against DDoS attacks in vehicular networks

Abstract

In recent years, the frequently reported incidents of Distributed Denial of Service assaults on vehicular networks in various countries have made researchers find new protective solutions. DDoS attacks can propagate through the charging points for electric vehicles in a charging station and affect the production of critical infrastructures such as electric grids. Existing solutions are efficient in attack detection; however, current systems do not offer multi-level protection, and zero-day vulnerabilities are prone to escape from the detection systems.In this paper, we address the problems mentioned above and introduce the first Machine Learning (ML)–based DDoS protective solution to combine prevention and detection mechanisms in vehicular networks. To be more specific, our proposed model is the first to consider the adaptive traffic threshold to generate the alarm for a suspicious amount of traffic flow in an Intrusion Detection Prevention System (IDPS). We call our proposed approach Protecting vEhicular neTworks against distRibuted deniAl of service attacKs (PETRAK). PETRAK uses four functions: prevention, alarm, training, and detection. The alarming system uses the flow parameters and activates the detection module to detect malicious packets. The prevention system works in two modes: immediate and future. PETRAK uses logistic regression to identify incoming packets and signatures of malicious packets to prevent future attacks. We also show that our proposed model is implacable towards advanced post-quantum cryptography-based traffic and also able to analyse side-channel attacks. We run a comprehensive set of experiments to test PETRAK on our synthesized dataset, KDDCUP’99 dataset, CIC-MalMem-2022, and the ToN-IoT dataset. We observe that PETRAK shows an accuracy of 99%. The results claim the efficiency of PETRAK in detecting and preventing DDoS attacks in vehicular networks.