Machine learning-based blockchain technology for protection and privacy against intrusion attacks in intelligent transportation systems
Yakub Kayode Saheed · Institution of Engineering and Technology eBooks · 2022
Intelligent transportation system (ITS) is a rapidly growing field of technology that combines network connectivity, modern sensors, control system, and data processing technologies to improve our daily life. With the growing popularity of ITS, concerns about its security have garnered considerable attention. SQL injection, denial of service, and ransomware assaults are all prevalent types of threats in an ITS. In automation/transportation systems, privacy and trust are also significant challenges. Today, everyone requires a vehicle to move around. Together with this, data security is crucial in a computerization system since the vehicle's user data is transferred to the extra operator via the web through wireless devices and routes such as radio channels, optical fiber, and so on. Certainly, every device is linked to the internet and one another, constituting the Internet of Things (IoT). Even as the network transitions to wireless devices, numerous risks to autonomous cars/vehicles have developed into a serious issue for service providers and car owners. The bulk of these attacks is detectable and preventable using a variety of intrusion detection methods. The blockchain and, more broadly, peer-to-peer techniques may be critical in the development of decentralized and data-intensive applications that run on billions of devices while maintaining user privacy. To solve these problems, this chapter proposes a machine learning (ML)-based blockchain intrusion detection for protection and privacy against intrusion attacks in ITS. The blockchain was used for aiding information exchange in ITS. As a result of the immutable and decentralized nature of blockchain-enabled ITS systems, a variety of desirable qualities such as security, decentralization, transparency, automation, and immutability are expected to exist. The experimental analysis was performed on the UNSWB-NB15 dataset. The results obtained reached an accuracy that is more than 99%. The AUC, recall, Mathew Correlation Coefficient (MCC), and training time metrics were also used to evaluate the performance of the models.