Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications.Internet of Things(IoT),fog computing,edge computing,cloud computing,and the edge of things are the spine of all real-time and scalable applications.Conspicuously,this study proposed a novel framework for a real-time and scalable application that changes dynamically with time.In this study,IoT deployment is recommended for data acquisition.The Pre-Processing of data with local edge and fog nodes is implemented in this study.The thresholdoriented data classification method is deployed to improve the intrusion detection mechanism’s performance.The employment of machine learningempowered intelligent algorithms in a distributed manner is implemented to enhance the overall response rate of the layered framework.The placement of respondent nodes near the framework’s IoT layer minimizes the network’s latency.For economic evaluation of the proposed framework with minimal efforts,EdgeCloudSim and FogNetSim++simulation environments are deployed in this study.The experimental results confirm the robustness of the proposed system by its improvised threshold-oriented data classification and intrusion detection approach,improved response rate,and prediction mechanism.Moreover,the proposed layered framework provides a robust solution for real-time and scalable applications that changes dynamically with time.