Deep Learning-based Intrusion Detection System for IoT-based Blockchain System

Security and privacy concerns, which are made worse by the growing number of Internet-connected devices, are the primary obstacles to the Internet of Things (IoT) widespread implementation. Everyone is now very concerned about Internet of Things security, including businesses, governments, and consumers. Even though no system can ever be completely protected from attacks, effective system defense depends on real-time threat detection. There is a dearth of studies on intrusion detection systems that work well in IoT-based blockchain systems. In this study, we present a novel approach to intrusion detection in the Internet of Things-based blockchain systems by employing the DL (Deep Learning) subfield of machine learning to identify security abnormalities. This detection platform provides security as a service and allows interoperability with numerous network communication protocols utilized by the Internet of Things. We go into great details about the suggested system's architecture and intrusion detection method. Real network traces are evaluated along with simulated data to illustrate the scalability of the proposed intrusion detection system to prove its viability. Our results confirm that the proposed intrusion detection system can correctly detect real intrusions.