A Hybrid Learning-Based Network Model for Security Establishment in IoT Environment

Internet of Things (IoT) is an innovative idea, combines the IoT with social platforms to enable inanimate objects to connect. Despite this, customers are hesitant to accept this new norm. Users are worried that personnel data will be exposed and that privacy will be violated. IoT won't take the lead as a technology until tested with proven methods to strengthen reliable connectivity between nodes. The complexity of maintaining data privacy as a result makes it much more challenging to offer top-notch services and complete security. Here, security and data privacy seems to be challenging where this problem has been examined in several existing works. They created a variety of models based on various criteria and classification strategies for safe nodes on the IoT network. On the other hand, earlier attempts did not provide a method for identifying fake nodes or discriminating between different attack kinds. To segregate hostile nodes from the network and identify assaults that initiated, revolutionary hybrid network model (HNM) with embedded CNN and LSTM is recommended. To get the optimum performance in the suggested research, performance measures including accuracy, precision, recall, and loss are examined and compared with the current methodologies.