A Deep Learning and Optimization Method for Detecting Network Intrusion in IOT

The accessibility and maintenance of the internet of things depend heavily on security. Numerous investigations have looked into the use of anomalies intrusion detection in various applications. Researchers and engineers must be able to interpret the complicated structure from imprecise information, recognize the dynamic anomaly patterns, and find anomalies when there aren't enough labels to describe them. As a result, using deep learning techniques rather than conventional shallow learning methods is necessary to improve anomalous detection capability. Large computations are also necessary due to the enormous volume of data generated by the IoT's many connected devices. This article offers a survey on anomalous intrusion detection using deep learning techniques, with a focus on IoT devices utilized in real-world issues that have limited resources. The results of the research that were evaluated demonstrated that deep learning is better to traditional learning when it comes to high identification efficiency and low rate of false alarms. Therefore, it's also strongly advised to carry out additional research employing deep learning algorithms towards reliable Detection.