DBN-protected material Enhanced intrusion prevention sensor system defends against cyber attacks in the IoT devices

Abstract

By linking objects globally, the Internet of Things (IoT) has transformed technology with the goal of achieving an unparalleled degree of intelligence that will benefit mankind in many areas. Resilient applications in safety, healthcare, and industrial processes rely heavily on continuous connectivity and interaction with surrounding objects. But the IoT ecosystem's enormous number of businesses and apps significantly raises the possibility of unwanted access, raising worries about cyberattacks. It is important to protect symmetrical networks used in modern communication from these dangers. With an emphasis on a sophisticated intrusion detection and prevention system based on Deep Belief Symmetrical Networks (DBNs), this study investigates cutting edge techniques and tactics for preventing security breaches. Our research specifically investigates possibly dangerous behaviour within IoT symmetrical networks and attempts to determine its source. We present a DBN-protected material improved symmetrical intrusion prevention sensor system that improves IoT device security. We improve the system's capacity to identify and prevent cyber-attacks by exploiting DBNs. We compare the suggested method's performance to industry-standard Intrusion Detection Systems (IDS) algorithms and Domain Generation Algorithms (DGAs) to assess its effectiveness. We create results that demonstrate the usefulness of our method in fighting against cyber-attacks in the IoT environment through rigorous research and testing. This study advances the development of safe IoT Symmetrical devices and encourages the full realization of their promise in allowing a connected and intelligent world.

Keywords

• Internet of things (IoT)
• Deep belief symmetrical networks (DBNs)
• security
• Intrusion detection
• Intrusion prevention
• Cyber-attacks