Deep neural network based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities

An anomaly exposure system's foremost objective is to categorize the behavior of the system into normal and untruthful actions. To estimate the possible incidents, the administrators of smart cities have to apply anomaly detection engines to avert data from being jeopardized by errors or attacks. This article aims to propose a novel deep learning-based framework with a dense random neural network approach for distinguishing and classifying anomaly from normal behaviors based on the type of attack in the Internet of Things. Machine learning algorithms have the improbability to explore the performance, compared with deep learning models. Distinctively, the examination of deep learning neural network architectures achieved enhanced computation performance and deliver desired results for categorical attacks. This article focuses on the complete study of experimentation performance and evaluations on deep learning neural network architecture for the recognition of seven categorical attacks found in the Distributed Smart Space Orchestration System traffic traces data set. The empirical results of the simulation model report that deep neural network architecture performs well through noticeable improvement in most of the categorical attack.

Automated summary

The primary objective of an anomaly exposure system is to categorize system behavior into normal and untruthful actions. To prevent data from errors or attacks, administrators in smart cities must employ anomaly detection engines. This article proposes a novel deep learning-based framework utilizing a dense random neural network approach to distinguish and classify abnormal behaviors from normal behaviors based on the type of attack in the Internet of Things.