Intrusion detection system using distributed multilevel discriminator in GAN for IoT system

The artificial intelligence-based Internet of vehicles (IoV) systems are highly susceptible to cyber-attacks. To reap the benefits from IoV applications, the network must be protected against numerous security threats. Attacks that have been reported by attackers within the IoV system are found using intrusion detection systems (IDSs). Instead of relying on a centralized server, a distributed classifier is required for large-scale networks like IoV.Datasets are kept secret because managing sensitive information is a difficult task. Due to privacy concerns, devices are not intended to share information among themselves. This paper proposes a multilevel discriminator for the distributed model of IDS with generative adversarial networks (GANs) for IoV devices. Without relying on a centralized controller, the suggested architecture leverages a multilevel distributed GAN model to identify abnormal behavior. Each IoV device in the proposed architecture communicates with its neighbors in a peer-to-peer fashion to monitor its data and identify both internal and external threats on other devices nearby. Additionally, the proposed design makes sure that datasets do not need to be shared with other IoV devices, ensuring the privacy of all IoV system data, including sensitive data-like vehicular data.