Holographic Counterpart Technologies Into Secure Internet of Things Consumer Devices to Maintain Security and Privacy of IoT Devices

Mobile Edge Computing (MEC) servers or third-party-regulated cloud are coupled to resource constrained devices to govern the consumer Internet of things (CIoT). Automation, consumer behavior research, device quality improvement, and other fields have recently made extensive use of machine learning (ML). In this research a novel technique in IoT networks in consumer devices security and privacy analysis using holographic model with machine learning analysis is proposed. The consumer device based IoT network behavior has been analyzed using a reinforcement adversarial fuzzy gaussian encoder model is deployed for analyzing consumer device based IoT network behavior. The mont-carlo multi-layer blockchain model is also deployed using holographic key encryption to analyze consumer IoT network security. The experimental analysis has been carried out in terms of accuracy, random precision, data integrity, computational efficiency, f1-score. Proposed method random precision was 96%, data integrity was 95%, F1-SCORE was 93%, accuracy was 97%, and Computational efficiency was 98% using CIC IoT and MQTT-IoT-IDS datasets. The study shows the possibility of identifying a hologram transparency signal that gives rise to desired scattered waveform by using resulting data to create associated forward scattering map as well as its unique single method.